{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   station           7750 non-null   float64\n",
      " 1   Date              7750 non-null   object \n",
      " 2   Present_Tmax      7682 non-null   float64\n",
      " 3   Present_Tmin      7682 non-null   float64\n",
      " 4   LDAPS_RHmin       7677 non-null   float64\n",
      " 5   LDAPS_RHmax       7677 non-null   float64\n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64\n",
      " 8   LDAPS_WS          7677 non-null   float64\n",
      " 9   LDAPS_LH          7677 non-null   float64\n",
      " 10  LDAPS_CC1         7677 non-null   float64\n",
      " 11  LDAPS_CC2         7677 non-null   float64\n",
      " 12  LDAPS_CC3         7677 non-null   float64\n",
      " 13  LDAPS_CC4         7677 non-null   float64\n",
      " 14  LDAPS_PPT1        7677 non-null   float64\n",
      " 15  LDAPS_PPT2        7677 non-null   float64\n",
      " 16  LDAPS_PPT3        7677 non-null   float64\n",
      " 17  LDAPS_PPT4        7677 non-null   float64\n",
      " 18  lat               7752 non-null   float64\n",
      " 19  lon               7752 non-null   float64\n",
      " 20  DEM               7752 non-null   float64\n",
      " 21  Slope             7752 non-null   float64\n",
      " 22  Solar radiation   7752 non-null   float64\n",
      " 23  Next_Tmax         7725 non-null   float64\n",
      " 24  Next_Tmin         7725 non-null   float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/9/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.051193</td>\n",
       "      <td>99.668961</td>\n",
       "      <td>27.872808</td>\n",
       "      <td>22.907420</td>\n",
       "      <td>11.017837</td>\n",
       "      <td>44.002020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5925.883789</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>22.0</td>\n",
       "      <td>7/10/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.196007</td>\n",
       "      <td>95.168205</td>\n",
       "      <td>28.097980</td>\n",
       "      <td>24.510159</td>\n",
       "      <td>8.374849</td>\n",
       "      <td>38.782242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5102</td>\n",
       "      <td>127.086</td>\n",
       "      <td>21.9668</td>\n",
       "      <td>0.133200</td>\n",
       "      <td>5772.487305</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/12/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.027298</td>\n",
       "      <td>99.209839</td>\n",
       "      <td>24.078120</td>\n",
       "      <td>21.866817</td>\n",
       "      <td>8.543768</td>\n",
       "      <td>9.371270</td>\n",
       "      <td>...</td>\n",
       "      <td>5.055660</td>\n",
       "      <td>1.347418</td>\n",
       "      <td>0.980052</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5893.265625</td>\n",
       "      <td>23.2</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.891193</td>\n",
       "      <td>94.747780</td>\n",
       "      <td>29.195536</td>\n",
       "      <td>23.236973</td>\n",
       "      <td>10.881031</td>\n",
       "      <td>79.349271</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.057358</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.785000</td>\n",
       "      <td>5812.293457</td>\n",
       "      <td>27.6</td>\n",
       "      <td>21.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>15.0</td>\n",
       "      <td>7/18/2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.795406</td>\n",
       "      <td>83.902847</td>\n",
       "      <td>31.480089</td>\n",
       "      <td>25.607262</td>\n",
       "      <td>8.995135</td>\n",
       "      <td>26.022306</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008702</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>126.937</td>\n",
       "      <td>30.0464</td>\n",
       "      <td>0.855200</td>\n",
       "      <td>5681.875000</td>\n",
       "      <td>30.7</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7629</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8/26/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.755058</td>\n",
       "      <td>83.340240</td>\n",
       "      <td>25.842338</td>\n",
       "      <td>18.532986</td>\n",
       "      <td>4.926595</td>\n",
       "      <td>97.230757</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.505500</td>\n",
       "      <td>4602.118164</td>\n",
       "      <td>26.1</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7682</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8/28/2017</td>\n",
       "      <td>26.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>29.959215</td>\n",
       "      <td>90.116638</td>\n",
       "      <td>23.135079</td>\n",
       "      <td>17.282587</td>\n",
       "      <td>9.292264</td>\n",
       "      <td>75.430868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4518.488770</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7707</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8/29/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.392651</td>\n",
       "      <td>75.728195</td>\n",
       "      <td>22.223247</td>\n",
       "      <td>15.954970</td>\n",
       "      <td>4.764492</td>\n",
       "      <td>37.786237</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4697</td>\n",
       "      <td>126.910</td>\n",
       "      <td>52.5180</td>\n",
       "      <td>1.562900</td>\n",
       "      <td>4478.937012</td>\n",
       "      <td>23.1</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.4562</td>\n",
       "      <td>126.826</td>\n",
       "      <td>12.3700</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>17.4</td>\n",
       "      <td>11.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.6</td>\n",
       "      <td>29.9</td>\n",
       "      <td>98.524734</td>\n",
       "      <td>100.000153</td>\n",
       "      <td>38.542255</td>\n",
       "      <td>29.619342</td>\n",
       "      <td>21.857621</td>\n",
       "      <td>213.414006</td>\n",
       "      <td>...</td>\n",
       "      <td>21.621661</td>\n",
       "      <td>15.841235</td>\n",
       "      <td>16.655469</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.135</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>5.178230</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>38.9</td>\n",
       "      <td>29.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  \\\n",
       "225       1.0   7/9/2013           NaN           NaN    70.051193   \n",
       "271      22.0  7/10/2013           NaN           NaN    72.196007   \n",
       "300       1.0  7/12/2013           NaN           NaN    95.027298   \n",
       "450       1.0  7/18/2013           NaN           NaN    60.891193   \n",
       "464      15.0  7/18/2013           NaN           NaN    52.795406   \n",
       "...       ...        ...           ...           ...          ...   \n",
       "7629      5.0  8/26/2017           NaN           NaN    43.755058   \n",
       "7682      8.0  8/28/2017          26.3          18.1    29.959215   \n",
       "7707      8.0  8/29/2017           NaN           NaN    44.392651   \n",
       "7750      NaN        NaN          20.0          11.3    19.794666   \n",
       "7751      NaN        NaN          37.6          29.9    98.524734   \n",
       "\n",
       "      LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_Tmin_lapse   LDAPS_WS    LDAPS_LH  \\\n",
       "225     99.668961         27.872808         22.907420  11.017837   44.002020   \n",
       "271     95.168205         28.097980         24.510159   8.374849   38.782242   \n",
       "300     99.209839         24.078120         21.866817   8.543768    9.371270   \n",
       "450     94.747780         29.195536         23.236973  10.881031   79.349271   \n",
       "464     83.902847         31.480089         25.607262   8.995135   26.022306   \n",
       "...           ...               ...               ...        ...         ...   \n",
       "7629    83.340240         25.842338         18.532986   4.926595   97.230757   \n",
       "7682    90.116638         23.135079         17.282587   9.292264   75.430868   \n",
       "7707    75.728195         22.223247         15.954970   4.764492   37.786237   \n",
       "7750    58.936283         17.624954         14.272646   2.882580  -13.603212   \n",
       "7751   100.000153         38.542255         29.619342  21.857621  213.414006   \n",
       "\n",
       "      ...  LDAPS_PPT2  LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM  \\\n",
       "225   ...    0.036680    0.000000    0.000000  37.6046  126.991  212.3350   \n",
       "271   ...    0.007261    0.000000    0.000000  37.5102  127.086   21.9668   \n",
       "300   ...    5.055660    1.347418    0.980052  37.6046  126.991  212.3350   \n",
       "450   ...    0.000000    0.000000    0.057358  37.6046  126.991  212.3350   \n",
       "464   ...    0.000000    0.000000    0.008702  37.5507  126.937   30.0464   \n",
       "...   ...         ...         ...         ...      ...      ...       ...   \n",
       "7629  ...    0.000000    0.000000    0.000000  37.5507  127.135   35.0380   \n",
       "7682  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7707  ...    0.000000    0.000000    0.000000  37.4697  126.910   52.5180   \n",
       "7750  ...    0.000000    0.000000    0.000000  37.4562  126.826   12.3700   \n",
       "7751  ...   21.621661   15.841235   16.655469  37.6450  127.135  212.3350   \n",
       "\n",
       "         Slope  Solar radiation  Next_Tmax  Next_Tmin  \n",
       "225   2.785000      5925.883789       23.4       22.0  \n",
       "271   0.133200      5772.487305       26.1       24.1  \n",
       "300   2.785000      5893.265625       23.2       20.5  \n",
       "450   2.785000      5812.293457       27.6       21.8  \n",
       "464   0.855200      5681.875000       30.7       23.4  \n",
       "...        ...              ...        ...        ...  \n",
       "7629  0.505500      4602.118164       26.1       17.9  \n",
       "7682  1.562900      4518.488770        NaN        NaN  \n",
       "7707  1.562900      4478.937012       23.1       16.3  \n",
       "7750  0.098475      4329.520508       17.4       11.3  \n",
       "7751  5.178230      5992.895996       38.9       29.8  \n",
       "\n",
       "[164 rows x 25 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "Data[Data.isnull().T.any()]        #取出所有包含空值的行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e074f06a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07e08b20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07ed0ca0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07fd10a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e08065d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e080a62e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e08224550>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e082f6160>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e083ca5b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TWH1xod3h2GJ2TiKfm5PFvspG1u86hTG6gphSvQ2aAETECdwLrACKgVUiUtyr2AqgyPpbDdznQ91fAP9ujJkH/Jv1Wo2Cn79ykLOtnfzi2jmEO0O3l+/8yalcXJTG9qP1bD5ca3c4SvkdX74dFgOlxpgyY0wnsA5Y2avMSuAx47UVSBKRrEHqGiDBep4InBrhuSigoa2LZ9/zdnvMzE60OxzbXT4zgzm5iby69zS7ys/aHY5SfsWXBJAD9Bw+UmFt86XMQHW/A9wjIuXAL4Ef9PXmIrLa6iIqqamp8SHc0PZOaS0eA/9wUWh2/fTmEOHaBbnkp8Ty7M4KSo7V2x2SUn7DlwTQ19Wz3h2q/ZUZqO43gDuNMXnAncD/9PXmxpgHjDGLjDGL0tLSfAg3dLV2uth+tJ5r5maTlxxjdzh+I8zp4GtLJ5IUHc5tj++kukkXkVEKfEsAFUBej9e5fLK7pr8yA9W9EXjeev4M3u4iNQJby+rodHv4x0v0139vMRFhfHXJJJraXdzx+Ps6PFQpfEsAO4AiESkQkQjgemB9rzLrga9bo4GWAg3GmMpB6p4CLrGeXwYcHuG5hLQut4d3j9QxPTOe6ZkJg1cIQZmJUVwzN5vtx+q56eHtPLHtxEd/SoWiQe8DMMa4ROQO4FXACTxsjNkrImus/WuBDcCVQCnQCtw8UF3r0P8A/FpEwoB2vKOH1DCV1bTQ2ulmSUGy3aH4tbl5SRyvb+WdI3VMzYgP+nsklBqITzeCGWM24P2S77ltbY/nBrjd17rW9r8BC4cSbCgb7Ffq/tONRDgdFKbFjVNEgWvFrEzKapp5dmcF37qsiFhdQ0CFqNAdJB5EjDEcqGykKCMupMf9+yrc6eC6RXm0drh54f2TepOYCln60ycInDzbRmO7ixlZ3r5/7dMeXHZSNMuKM3hl72l2VzTwVbsDUsoG+nMxCOyvbEKAadqfPSQXFqWSOyGaF3ef4kxLp93hKDXuNAEEgQOnG5mUEqN92UPkEOEL83No63Lz05f22x2OUuNOE0CAO9PaSWVD+0fdP2poshKjubgojed2VvD2Yb3TXIUWTQAB7uDpJgBm6Nj/Yfv09HQKU2P5Xy98SFun2+5wlBo3mgAC3In6VhKiwkiND97lHsdauNPB//vibMrr2/jVXw/ZHY5S40YTQICrONNK7gSd92eklhamsGpxHg+9Xcaekw12h6PUuNAEEMDaOt3UNneSOyHa7lCCwl0rZpASF8n3n9uNS+cKUiFAE0AAqzjbCqAtgFGSGB3OT66eyd5Tjfxx63G7w1FqzGkCCGAVZ9oAyEnSFsBouXJ2JhcVpfIfrx2ipqnD7nCUGlOaAAJYRX0rqXGRREc47Q4laIgI/37NTNpdbn62Qe8NUMFNE0CAMsZQcaaNPO3/H3WFaXGsvriQ598/ybayOrvDUWrM6K2jAaqx3UVTh0svAI+S3vMnpcVFkZMUzb/9eS8vfutCnWRPBSX9Vx2gyuv1AvBYighz8OOrizlY1cSj7x6zOxylxoS2AAJUxZk2nCJkJUbZHUrQqmnqYFpGPPe8ehCX25AQHQ7ADUsm2hyZUqNDWwABquJsK5mJUYRp18SYERGumpOF22N4eU+l3eEoNer02yMAGWOoPNtOtg7/HHMpcZFcVJTKrooGTljdbkoFC00AAai5w0Vbl5uMBJ3/ZzxcPDWN+KgwXtp9SlcPU0FFE0AA6r5BKU0ngBsXkWFOLi/OoPxMG7srdJ4gFTw0AQSgaisBpMfrBeDxMn/iBLITo3hl72nau3TKaBUcNAEEoOqmDiLDHCRE6SCu8eIQ4crZWTS0dfGHLTpPkAoOPiUAEVkuIgdFpFRE7upjv4jIb6z9u0VkgS91ReSb1r69IvKLkZ9OaKhpaictPhIRsTuUkFKYFkdRehz3biylsb3L7nCUGrFBE4CIOIF7gRVAMbBKRIp7FVsBFFl/q4H7BqsrIp8GVgJzjDEzgV+OxgmFguqmDtK1/98Wl8/M5GxrFw9uLrM7FKVGzJc+hMVAqTGmDEBE1uH94t7Xo8xK4DHjHSKxVUSSRCQLyB+g7jeAu40xHQDGmOrROaXg1t7lpqndpf3/NslJimZ2TiL3byojLjKM+Ci9OUwFLl+6gHKA8h6vK6xtvpQZqO5U4CIR2SYim0TkvL7eXERWi0iJiJTU1Oii3dU6Ash2y2Zk4PJ42HxI/z2qwOZLAuiro7n3YOj+ygxUNwyYACwFvgs8LX10ahtjHjDGLDLGLEpLS/Mh3OBW3dgOoF1ANkqNj2ReXhLbj9XT3OGyOxylhs2XBFAB5PV4nQuc8rHMQHUrgOeN13bAA6T6HnpoqmnqIMwhTIiNsDuUkHbJ1HRcbsM7pbV2h6LUsPmSAHYARSJSICIRwPXA+l5l1gNft0YDLQUajDGVg9T9E3AZgIhMBSIA/d80iOqmDlLjInHoCCBbpcVHMisnkS1ldbR2aitABaZBE4AxxgXcAbwK7AeeNsbsFZE1IrLGKrYBKANKgQeB2waqa9V5GCgUkT3AOuBGo/fZD6raGgKq7HfptDQ6XR62HNFFY1Rg8ulOImPMBrxf8j23re3x3AC3+1rX2t4JfG0owYa6TpeHs61dLJikCcAfZCVGMyMrgXeP1NHU3vXRiCClAoXeCRxAaps7MOgUEP7k09PSaOty88etJwYvrJSf0QQQQGqbvUNAU+P0ArC/yJ0QQ1F6HA+9XUZbp84RpAKLJoAAUt/SCUCyjgDyK5+elk5dSydPbtdWgAosmgACSF1LJ/GRYUSGOe0ORfWQnxrLkoJk7t98hA6XtgJU4NAEEEDqmjv117+f+uZlRVQ1dvBMSYXdoSjlM00AAaS+pYMU7f/3SxdMSWFeXhJrNx2hy+2xOxylfKIJIEB0uT00tru0BeCnntxezpzcRCrOtHHXc7t5YtsJntim1wSUf9MEECC6LwCnxOo9AP5qWkY82UlRbDxYg0fvaVQBQBNAgKhr1hFA/k5EPhoRpGsHq0CgCSBA1Ld47wHQawD+bUZWAhkJkWw8WK2tAOX3NAEEiLqWTqLCHcRE6DrA/swhwqXT0qlu6mDvqUa7w1FqQJoAAkR9S6f2/weI2TmJpMZ5WwE6v6HyZ5oAAkRdi94DECi8rYA0KhvaeWO/rnSq/JcmgADQ5fZwtrVT+/8DyNzcJJJjI/jtm4e1FaD8liaAAHDqbBseAynaAggYTodw6dQ0dlU08FdtBSg/pQkgAByrawUgWa8BBJT5EydQmBrLL145gEvvDlZ+SBNAADhR1wJoCyDQOB3C95ZP43B1M8/t1DmClP/RBBAAjtW1Eu4U4qN0CGiguWJmJvMnJvGr1w/regHK72gCCADH61pJjo1AdCH4gCMi3LV8Oqcb23no7TK7w1HqHJoAAsDxuhbt/w9gSwpTWD4zk3s3llJxptXucJT6iCYAP+fxGE7Ut2r/f4D70dXFAPz0xf02R6LUxzQB+LmqpnY6XB69CSzA5SRF883Linhl72k2HtRhoco/+JQARGS5iBwUkVIRuauP/SIiv7H27xaRBUOo+y8iYkQkdWSnEpyOW0NA9SawwPf3FxVQmBrLT9bv1aUjlV8YNAGIiBO4F1gBFAOrRKS4V7EVQJH1txq4z5e6IpIHLAN05Yx+nOhOAHoNIOBFhjn5yTUzOVbXyoOb9YKwsp8v4woXA6XGmDIAEVkHrAT29SizEnjMeO953yoiSSKSBeQPUvdXwPeAP4/CuQSlY3UthDmExOhwu0NRw9DXqmBXzs7kd2+V8vn5OeROiLEhKqW8fOkCygHKe7yusLb5UqbfuiJyDXDSGLNroDcXkdUiUiIiJTU1NT6EG1yO17eSOyEap0OHgAaLf/1cMYLwf17cN3hhpcaQLwmgr2+e3rNb9Vemz+0iEgP8EPi3wd7cGPOAMWaRMWZRWlraoMEGm+N1LUxKibU7DDWKspOi+eZnpvDq3irePFBldzgqhPmSACqAvB6vc4FTPpbpb/tkoADYJSLHrO07RSRzKMEHO2MMx+tamZSi3QTB5IltJ4iLDCM9PpI7n9rF7/92VBeQV7bwJQHsAIpEpEBEIoDrgfW9yqwHvm6NBloKNBhjKvura4z50BiTbozJN8bk400UC4wxp0frxILBmdYumtpd2gIIQmEOB19ckEtjWxev7dNWgLLHoBeBjTEuEbkDeBVwAg8bY/aKyBpr/1pgA3AlUAq0AjcPVHdMziQIHbcmgZuUHEN1U4fN0ajRNjE5hqWTU9h6pI65uYl2h6NCkE+zixljNuD9ku+5bW2P5wa43de6fZTJ9yWOUNN9D0B+qiaAYHX5jAz2n2rk+fdPcuflU4kMc9odkgoheiewHzte14oIOlQwiEWGO1k5L4fqpg7u23jE7nBUiNEE4MeO17WQmRBFVLj+Kgxm0zLjmZubyL1vlXK4qsnucFQI0QTgx47X6wigUPG5OdnERYbx/ed24/HoGsJqfGgC8GPH61qYlKwjgEJBXGQYP7qqmJ0nzvLHbcftDkeFCF1iyk81d7iobe5korYAQkZbp5ui9Dh++tJ+mttdJMVEcMOSiXaHpYKYtgD81NEa7xDQyWnaAggVIsLKeTkYY1i/6xTewXVKjR1NAH6qrLYZgILUOJsjUeMpOTaCZcWZHDjdxK6KBrvDUUFOE4CfKqtpQQS9CByCzp+cQt6EaNbvOsmps212h6OCmCYAP3W0toWcpGgdAhqCHCJctygPjwe+++wuHRWkxowmAD9VVttMYZp2/4SqlLhIPjc7i3dK6/j9u8fsDkcFKU0AfsgYw9GaFgpT9QJwKFuUP4HPzsjg5y8fYFf5WbvDUUFIE4Afqm7qoKXTTaGOAAppIsI9184hLT6S2x7fydnWTrtDUkFGE4AfKrOGgBZoCyDkTYiN4N6vLqC6qZ1/flqvB6jRpQnAD3UPAdVrAApgXl4SP7qqmDcOVPNffz1kdzgqiOidwH7oaE0LUeEOshKi7A5F+Ym/WzqJPScb+M2bpUxOj2PlvN7Lcis1dNoC8ENltS3kp8Ti0IXglUVE+OnnZ7OkIJnvPrub946fsTskFQQ0Afiho7UtegFYfUJEmIO1X1tIdmIUf//oDkqrm+0OSQU4TQB+psvt4UR9q14AVn2aEBvBo7cspstt+NJ97/Lfb5XyxLYTuqi8GhZNAH7mRH0rbo+hUOcAUv2YlBLLTefn097l5vfvHqO1w2V3SCpA6UVgP9M9C2iBdgEp6PeXfXZSNH/3qUk88s4xHt1yjFsvLBzfwFRQ0BaAnzlS4+3XnawtADWIwtQ4rj8vj4ozbTy+7TidLo/dIakAownAz+yvbCQrMYrEmHC7Q1EBoDg7kS/Mz+FwdTN3Pv0Bbr1RTA2BT11AIrIc+DXgBB4yxtzda79Y+68EWoGbjDE7B6orIvcAVwOdwBHgZmNMyE94sr+yiRlZCXaHoQLIovxk2rrcvLS7ktNn2/nCghwc8vEQYl1VTPVn0BaAiDiBe4EVQDGwSkSKexVbARRZf6uB+3yo+zowyxgzBzgE/GDEZxPg2rvclNY0MyMr3u5QVIC5qCiNy6an896JM7y0u1JXE1M+8aULaDFQaowpM8Z0AuuAlb3KrAQeM15bgSQRyRqorjHmNWNM9/CFrUDuKJxPQCutbsbtMdoCUMPymenpXDA5hS1ldby+r8rucFQA8KULKAco7/G6AljiQ5kcH+sC3AI85UMsQW1fZSOAJgA1LCLClbOz6HR72HiohsgwB5dMS7c7LOXHfGkB9DUfQe/2ZX9lBq0rIj8EXMDjfb65yGoRKRGRkpqaGh/CDVz7KxuJDneSn6JDQNXwdC8sPzc3kVf3VbHlSK3dISk/5ksCqADyerzOBU75WGbAuiJyI3AV8FXTT6elMeYBY8wiY8yitLQ0H8INXPtONTItMx6nzgGkRsAhwrUL85iRlcBfdlfyTEn54JVUSPKlC2gHUCQiBcBJ4Hrghl5l1gN3iMg6vF08DcaYShGp6a+uNTro+8AlxpjWUTmbAGaMYX9lI5+bk6239asRczqEVefl8djW43z/ud1ERzi5ak623WEpPzNoAjDGuETkDuBVvEM5HzbG7BWRNdb+tcAGvENAS/EOA5N4lYoAAAz5SURBVL15oLrWoX8HRAKve0eRstUYs2Y0Ty6QnGpop7HdRbGOAFKjJMzp4GtLJrHhw0q+ve4DPAaumatJQH3Mp/sAjDEb8H7J99y2tsdzA9zua11r+5QhRRrk9p/6+ALwoSqd5VGNjogwB7+/+TxueWQH31n3Pi63hy8uCPkBd8qidwL7if3WCKDpOgJIjbLYyDB+f/N5LC1M4Z+f2cWj7x6zOyTlJ3QyOD+xr7KRSSkxxEXqR6JGV/c1pStmZnKmpZMfr99LXXMHdy6biogOOAhl2gLwE3tONVCsv/7VGAp3OrhhySQWTprAb94s5bvP7qbD5bY7LGUjTQB+4OTZNsrr2zgvP9nuUFSQczqEL87P4dufKeLZ9yr42kPbqGvusDssZRNNAH5g65E6AJYWptgciQoFIsKdy6by21Xz2V3RwNW//ZuuMRyiNAH4gS1ldSTFhDM9U4eAqvFz9dxsnl1zPk6n8JX7t3D/piN4dDrpkKIJwA9sOVLHkoJkHHoHsBpns3MTefGbF7GsOIOfvXyA6x/cyrHaFrvDUuNEh5zYrLy+lZNn2/iHiwrsDkWFkN53m184JZWYCCev7ati+a83853PTuWWCwqICNPfiMFMP12bbSnz9v9/anKqzZGoUCYiLJyUzOt3XsKFU9K4++UDLP+vzbx1oFrXFghi2gKw2dayOpJjIyhK1zWAlf0yE6N46MZFvHWgmv/94j5ufmQHBamxLJ+ZSV5yzDlldaWxwKcJwEbGGLYeqWNpofb/K//Qs2vo5gvy2X60nrcOVHPfpiPMzE7g8uJM0uIjbYxQjSZNADY6Ud/KqYZ21ujwT+WHwhwOzp+cysKJE3i7tJa/ldayv/IQCydN4LLpGXaHp0aBJgAb/WWXd2mES6fqqk3Kf0WGO/nsjAyWFqbw1oFqth+t54PyszS2d7HmkskkRofbHaIaJr0IbBOPx/B0SQVLC5OZmBIzeAWlbBYXGcbVc7O5c9lUZmYnsnbTES7+xVvcv+kI7V06pUQg0gRgk+3H6jlR38pXzssbvLBSfiQ5NoLrFuXx4jcvZF5eEj97+QCf/uVGnt5RjsvtsTs8NQTaBWSTp3eUEx8ZxvKZWXaHotSwzMxO5NFbFrPlSB13v3KA7z23m3teO8gVxRnMyEo4Z6ZRHTHknzQB2KCxvYsNeyr50oJcoiOcdoej1LD0HDF03cJcZmYl8Nq+Kv647QQTk2NYMSuTSSmxNkaoBqMJwAZ/2XWK9i4P1y3S7h8VHESEWTmJzMhKYOeJM7yxv4r7N5cxOyeRK2Zm2h2e6ocmgHHW1N7F794sZWZ2AnNyE+0OR6lR5XQI5+UnMzc3ibcP17D5cA37Khtp7nBx+6cnEx+lI4b8iV4EHmd3v3yAqsZ2/u8XZutqTCpoRYQ5+MyMDP5p2TTm5npHDF16z0bu23iEpvYuu8NTFk0A42jLkToe33aCWy8sYF5ekt3hKDXmEqPDuXZhHn+540KKsxP4+SsHuODuN/nZhv2U1TTbHV7I0y6gcVJe38r3ntvFpJQY/mnZNLvDUWpczc5N5A+3LmFX+Vnu23iEh/52lPs3l7Fw0gQ+OyODy6anMzUjTlvF40wCaaa/RYsWmZKSErvDGLKtZXXc9vhOutweHrl5MQsnTRiwfO+pepUKNo3tXXiM4cVdleyrbAQgKSacublJFGcnUJASy6SUGPJTY0mPj9TEMEIi8p4xZlHv7T61AERkOfBrwAk8ZIy5u9d+sfZfCbQCNxljdg5UV0SSgaeAfOAYcJ0xJmjWpTPG8H75WZ7eUc6z71UwKSWGB7++iMI0nfVTqYSocG5YMpHbLp1CZUMbmw7W8P6Js3xQfpZ3Smtx9ViZLDrcSVxkGAnRYSRGh5MQFU5CdDhXz80mMyGKzMQoUmIjdELFYRi0BSAiTuAQsAyoAHYAq4wx+3qUuRL4Jt4EsAT4tTFmyUB1ReQXQL0x5m4RuQuYYIz5/kCxjFcLwBhDl9vQ4XLT4fJ4/7r6ee5y09HlobXTRUNbF3UtnZTVtHDwdBOnG9uJDnfy+fnZ/ODKGST4OAJCWwAqlLk9xvt/qbmDupZO6po7ONvWRWNbF43tLprau+i9cmW4U0iP9yaDzIQokmMjPpEwosOdRIQ5iAxzEBHmINzpwBgwGIwBjzF4rEdjDG6PNxaPMbg9BrcxeDwGl8f76La+O8McgtPhsB7l40enEOH0vudHf07HxzE4HeOWtEbSAlgMlBpjyqwDrQNWAvt6lFkJPGa82WSriCSJSBbeX/f91V0JXGrVfxTYCAyYAIbrP18/xAvvV+DxeL/cPT0+bO9ra5vH0On2frEPV4TTQWpcBJmJUXyqMIXZuYlEhTt5cVflKJ6RUsHL6RCSYyO862T0sd9jDM0dLm9CaOuiof3j52daOzle10prp4v2LvcnEoW/CXPIR8kh3OnAISCI99Hq9nI4vNt+ce0clo7yzMG+JIAcoLzH6wq8v/IHK5MzSN0MY0wlgDGmUkT6nBJTRFYDq62XzSJy0IeYbXXY96KpQO2YBeI/QuE8Q+EcQc/TNp8a2c/jSX1t9CUB9NVG6Z1X+yvjS90BGWMeAB4YSp1AISIlfTXLgk0onGconCPoeQYbX+4DqAB6zlmQC5zyscxAdausbiKsx2rfw1ZKKTVSviSAHUCRiBSISARwPbC+V5n1wNfFaynQYHXvDFR3PXCj9fxG4M8jPBellFJDMGgXkDHGJSJ3AK/iHcr5sDFmr4issfavBTbgHQFUincY6M0D1bUOfTfwtIjcCpwAvjyqZxYYgrJrqw+hcJ6hcI6g5xlUAupGMKWUUqNH5wJSSqkQpQlAKaVClCYAG4jIchE5KCKl1l3QQUNEjonIhyLygYiUWNuSReR1ETlsPQ48GZIfEpGHRaRaRPb02NbveYnID6zP96CIXGFP1EPXz3n+REROWp/pB9ad/937Au48RSRPRN4Skf0isldEvm1tD7rPczCaAMaZNT3GvcAKoBhYJSLF9kY16j5tjJnXYxz1XcAbxpgi4A3rdaB5BFjea1uf52V9ntcDM606/2197oHgET55ngC/sj7TecaYDRDQ5+kC/tkYMwNYCtxunUswfp4D0gQw/j6aWsMY0wl0T48RzFbine4D6/HzNsYyLMaYzUB9r839nddKYJ0xpsMYcxTv6LjF4xLoCPVznv0JyPM0xlR2T1ZpjGkC9uOdtSDoPs/BaAIYf/1NmxEsDPCaiLxnTeMBvab9APqc9iMA9XdewfgZ3yEiu60uou6ukYA/TxHJB+YD2witzxPQBGCHEU+P4ecuMMYswNvFdbuIXGx3QDYIts/4PmAyMA+oBP7D2h7Q5ykiccBzwHeMMY0DFe1jW8Cc50A0AYw/X6bWCFjGmFPWYzXwAt6mcrBO+9HfeQXVZ2yMqTLGuI0xHuBBPu7+CNjzFJFwvF/+jxtjnrc2h8Tn2ZMmgPHny9QaAUlEYkUkvvs5cDmwh+Cd9qO/81oPXC8ikSJSABQB222Ib1R0fylavoD3M4UAPU9rAav/AfYbY/6zx66Q+Dx70jWBx9kg02MEugzgBWse8zDgCWPMKyKygwCf9kNEnsS7fkWqiFQAP6af6UysqVKexrvuhQu43RjjtiXwIernPC8VkXl4uz2OAf8IAX2eFwB/B3woIh9Y2/4XQfh5DkanglBKqRClXUBKKRWiNAEopVSI0gSglFIhShOAUkqFKE0ASikVojQBKKVUiNIEoEKCiDT3sa3nNMeHReT53jOzish8ETG9pwAWEbdVb4+IPCMiMdb2H1pTDO+29i8ZIKaNIrKo17ZLReTFXtseEZFrh3PeSg1EE4AKdd3THBcBTwFvikhaj/2rgL9Zjz21WfVmAZ3AGhH5FHAVsMAYMwf4LOdOIqaUX9EEoJTFGPMU8BpwA3w0ZcC1wE3A5SIS1U/Vt4EpQBZQa4zpsI5X2z03klL+SBOAUufaCUy3nl8AHDXGHAE2Alf2LiwiYXhnPv0Qb/LIE5FDIvLfInLJMGO4qMfqWx8A1wzzOEoNSBOAUufqOfXvKrwL9mA99uwGira+nEvwzhvzP8aYZmAhsBqoAZ4SkZuGEcPbPVbfmkeQTBao/I9OBqfUueYDJdaSf18CrhGRH+JNDCkiEm+tItVmfTmfw5okbCOwUUQ+xDur5CPjFbxSQ6EtAKUsIvIlvFNYP4n3Au4uY0yeMSbfGDMJ7/zx/S5nKSLTRKSox6Z5wPGxjFmpkdAWgAoVMdb0xt2654G/U0S+BsTinef+MmNMjYiswrugTU/PAd8A/tDPe8QBvxWRJLzTBpfi7Q4ayEsi0mU93wLc69PZKDUKdDpopZQKUdoFpJRSIUq7gJQaYyLyAlDQa/P3jTGv2hGPUt20C0gppUKUdgEppVSI0gSglFIhShOAUkqFKE0ASikVov4/3UTQxKI97oQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e083c2760>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e0852dd60>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tkkRGRCQC/RngjtBol4uAFuecJwcEV9c2c4EuKBoVMpMT+OGdC3HOccfKdRzXyBcZA8IZtvhzYDVwnpnVmdlHzeweM7sn1OQ5YC+wG/gB8LFhqzaKuv0Btta3MrdY/eejxeS8NB65ayFHWzv4yKPrOdnZE+2SRIbVkJf+O+eWD7HdAfdFrKIYteNIG509AeaWKNBHk/mlOXz3tvms+OkG7v5xJY/cVUFqoma8EG/ST3aY3jghqiP00efKmQX8v5vn8oknq/jwj9az8q6FpCVF/0e/77w0g9HcNBIOdQaHqbq2mZzUBEpyNcPiaPTeeUX8560Xsn7/Ce5cuY6mk13RLkkk4qJ/mDJKVNe2MLckG9MKRaPCQEe/ty4s5cnKWq76xsvcsaSM+68qH+HKRIaPjtDDcLKzh13H2tTd4gEXFGVx9yWT6ej28/2Xd/PCNq1LKt6hI/QwbDnUQsDB3JKsaJciETBpXBr3Lp3GY2sP8Lc/qeSWimI+9+7zSR/mfvX2zh421Taz82gbuxvaqTrYzOluPwEHyQlxpCbGUZiZTHFOCkXZKRoeK2dMgR6GN06IztERumfkpiVy79KpHGnp4KGX9/DSjgY+ftV0bqkojliQnurqoXJ/E6v3NrJ6TyObD7XgD61/mpkcT3JCHMkJcfjMaDrZRe2Jnjdn80xO8DF7YhbzSnMoG5ca9glUnTwd2xToYaiubaEoO0UzLHpMvM/Hp66dwdWzCvjK72r47G8284NVe/ng4lLeO6/ojL/fHd1+/nqwORTgx6mqbabb74j3GXNLsrnn8iksmjyOmYUZ5Gck8fN1tW97j9aObupOnGJrfSubDrVQeaCJSbmpXDmzgKn5aTqHI4NSoIehuq6ZC0t1dO5V80pz+NU9S/hTzTG+99JuvvK7Gh54fjsLJuWweHIuc4qzmZidQkFmEvE+HwHnaO3o5khLB7VNp6k53MqWQy1U1TbT2RPAZ3BBcTYfvWQKS6aOo2JSTtjDJDOTE5g1MYtZE7O4sSfAxoNNvLyzgZWv7WN6QTrvmVtEblriMP+LyGilQB/C8fZO6ppOc+eSsmiXIsPIzLh6VgFXzypg19E2frWxjtd3N/Lgi7sJDDELb0KcUZCZTMWkHKbkpzM5L43khDgADjWd5lDT6bOqKTHex0VTgn8Q1uw7wZ9qjvKtP+/kqpkFXDwtD5+O1qUPBfoQNr3Zf64TomNFeUEG//u6mUDwROaOI20cbe3guc2HcQ7MIDk+jsyUBLJTEshNTxzWcI2P83HJtDwuKMrimep6nt9yhN3H2rm5omTYT+TK6KKfhiFU1bbgM5hdpEAfi9KT4t9cbrD5VHdUa8lKSeD2xaWs39/EbzfV852/7OLWihKm5Gv2TwnSuKghVNc2M70gIyYuFRcxMxZNzuXepVNJivfxyKv7+PP2owS0OpOgQB+Uc47qumZdUCQxZ0JWCve9cxpzS7L5c80xHn1tP+2aTXLMU6APovbEaZpPdWuGRYlJSfFx3LygmPfPK2J/40m+++LuN8exy9ikQB9ElU6ISowzMyrKcrnn8qnE+Yxb/2s1P3ptnxbIHqPUMTyI6tpmkuJ9nFeYEe1SZJicyRS2sWxidgr3LZ3G6r3H+eKz29hwoIkHPjBHo2DGGB2hD6K6tpnZRVkkaE4NGQVSEuN4+EMVfOra83hu82Gu+9YrrNt3ItplyQhSUg2gxx9gS32LTojKqOLzGR9bOo1f/N0SDOPWh1fz5d9uo60jukMuZWQo0Aew82g7Hd0BzbAoo9LCslyev/9SbltUysrX9vHOr7/Ek+tr6fEHol2aDCN1sA3gjRkWL9QIFxml0pLi+bf3XcCtC0v44rPb+NRTm/jOi7v46MWTuekMrjLVTI+jhwJ9ANW1zWSnJlCamxrtUkTOyZzibH51zxL+uO0oD7+yly88u42vPb+dy6bn865ZBcwrzWFKXho+n+aGGe0U6AOoqm1mTrGWnBNvMDOuOb+Qa84vZOPBJp6pquf3W468uWJTelI8pbmpTMhKJi89ifTkeNKT4slIjmdrfStJ8T6S4uNITvifr5kpCZogLMYo0PtxqquHnUfbeNesgmiXIhJx80tz2H64jWnj0znW1smhplMcag5eRLftcCvtnT109gTo6hm8vz0hzijMTKY0N5UZEzL56eoDxIVxlK+umeGjQO/HlkOtoSXn1H8u3uWzYCAXZiazYNLbtweco6snQEe3n86eQPDW7aejJ8DpLj/H2jqob+5g7b4TvLankZSEOOaXZrNkap7mbI8SBXo/NmnJORF8Zm8ukzeYzh4/e461U13Xwuq9jby+p5ELirO4ZlYhOQr2EaVA70dVbTNF2SnkZ2jJOZGhJMXHvbnKUsvpbtbsbeT1PcfZVt/KxdPyuGLGeF2cN0IU6P2ormvW+HORs5CVksA15xdy0ZRxvLDtCC/vbGBbfSs3VxRTnKMRY8NNgd5HY3sntSdOc/vifjoVRWJcrMxNk5WSwE0LSphbks1TG+p46OU9XHN+IZdMy4t2aZ6m/wf1samuBdAJUZFIKB+fwf1XTmfmhEye33KEX26oo6PbH+2yPEuB3seGA03E+YwLtOScSESkJMZx26JSrp5VQHVtM7f812oa2zujXZYnhRXoZnatme0ws91m9pl+ti81sxYzqwrdPhf5UkfGuv0nmD0xU0vOiUSQmfHO88Zz+0WT2Hm0jZseWk3tiVPRLstzhgx0M4sDvgtcB8wClpvZrH6arnLOXRi6fSnCdY6Izh4/VbXNVJTlRrsUEU+aOSGTx+5eTGN7Jx/4/uvsPtYW7ZI8JZwj9EXAbufcXudcF/AEcOPwlhUdWw610NUTYKECXWTYLJiUyy/veQcBB8seXqtQj6BwAr0IqO31uC70XF9LzKzazJ43s/P7eyMzW2FmlWZW2dDQcBblDq91+4LrMVaU5US5EhFvO68wgydWXAQo1CMpnEDvb3KGvgsWbgQmOefmAt8Bnu7vjZxzDzvnKpxzFfn5+WdW6Qio3H+CKflp5KXrgiKR4TZtfPqboX77D9dxqPl0lCsa/cI581cHlPR6XAzU927gnGvtdf85M/uemeU5545HpszhFwg4Kg80cd3swmiXIuJpfcfKL19Uwg9W7eXGB1/j7y6b8pYBCZrI68yEc4S+Hig3s8lmlggsA57p3cDMCi00z6yZLQq9b2Okix1Ou46103K6WydERUbYhKwUPnRRGc2nuvjx6v109mic+tkaMtCdcz3A3wN/AGqAJ51zW83sHjO7J9TsJmCLmVUD3waWOef6dsvEtHX7g4vpLlKgi4y4yXlpLF9USn3zaR5fe5CegJbKOxthDbZ2zj0HPNfnuYd63X8QeDCypY2s9ftOMD4jiZLclGiXIjImzZyQyfvmFfPUxjp+taGOWypKhn6RvIWungGcc7y+p5ElU8dphSKRKFowKYeTnT38fusRMpLiuf0izal0JnTpP1BzuI3j7Z1cVq6Jg0Si7bLp+bxj6jhe29PIo6/ti3Y5o4oCHVi1Kzgm/tLy2BtKKTIWXX/BBGZNyOSLv93GH7ceiXY5o4YCHXhlVwPnFWRQmJUc7VJEhOBqSbdUlDC3OJt/eOKvVNU2R7ukUWHMB/rpLj/r9zVxqbpbRGJKYryPH95ZQX5GEnf/eL0m8wrDmA/0Nfsa6fIHuGy6ultEYk1eehKPfngR3X7HnT9aR/OprmiXFNPGfKCv2nmcpHgfiyZr/LlILJqan84P7qig7sRpVvx0gy48GsSYH7b4yq4GFk3OHXJlcxEZeb2nCXjf/CJ+sb6Wmx9azS0VJfh6DTHWFAFBY/oI/VDzaXYfa+dydbeIxLy5xdlcM6uATXUtvLDtaLTLiUlj+gj9+c2HAbhqZkGUKxGRcFw2PZ8Tp7p5eWcDOamJ6irtY0wH+m83HWZ2USZleWnRLkVEwmBmvGfuRFpOd/FM9SGyUxOYXpAR7bJixpjtcqk9cYqq2mb+5oKJ0S5FRM5AnM9YvrCUgsxkHl93kHrNo/6mMRvovwt1t9wwZ0KUKxGRM5WUEMedS8pISYjjJ6v3K9RDxm6gbzrM3OIsSnJTo12KiJyFzJQE7lxSRmdPgI88up7Wju5olxR1YzLQ9x8/yeZDLdwwR90tIqNZYVYyH1w8id3H2vnoo+s51dUT7ZKiakwG+rPVwRX0rld3i8ioN218Ot9cdiEbDjSx4icb6OgeuxcejblA7/YHeHzdQd4xdRxF2VrMQsQLbpgzkf+4aS6v7j7OfY9tpKtnbK54NOYC/Q9bj3C4pYOPXDw52qWISAR9YEExX3nvbP68/Rj/+IsqevxjL9TH3Dj0la/uY9K4VK6YMT7apYhIhN1+0SQ6uv185Xc1JCX4+I+b5hLnGzurkI2pQP/rwSY2HmzmC++ehW8MfZNFxpK7L51CR7efr/9xJ/6A4+s3zyUhbmx0RoypQP/Ra/vJSIrnJi0+K+IpvSfxAshNS+Ka8wv576p6dh5tZ9nCEhLifJ6fxGts/NkCag638rvNh1m2qIT0pDH1d0xkTLp8ej7vmTuRmsOtPPr6/jExpHFMBLpzjs8/s5XM5Hjue+e0aJcjIiPkoinjuLWihIMnTvHQy3s52OjtVY/GRKA/U13Pun0n+OdrZpCdmhjtckRkBM0tyeYjF0/mZGcP7/3ea7y663i0Sxo2ng/09s4evvpcDRcUZXHrQvWdi4xFk/PSuPfyqeSlJ3LHyrV898XdBAIu2mVFnKcDPRBw/PMvqznW1skXbzx/TA1fEpG3ystI4jcfuzh4EdIfdnD7I2upa/JWF4ynA/3rf9zB81uO8NnrZjK/NCfa5YhIlKUlxfOtZRfywPsvoLq2mWu/uYqfrTmA3yNH654N9MfXHuR7L+1h+aJS7r5UV4WKSJCZsWxRKb//+GXMKc7iX5/ewg3feZU1exujXdo581ygd/sDfPm32/jsbzZzaXkeX7rxfMzU1SIib1WSm8pjdy/mwdvm0Xq6m2UPr+G2H6zh1V3HcW50HrF7akD2lkMtfOnZbazbf4K73lHGv/zNzDFzhZiInDkz44Y5E7lqZgE/XX2AH6zay+2PrGV6QTo3LyjhxnkTGZ+RHO0ywxZWoJvZtcC3gDjgh865B/pst9D264FTwF3OuY0RrrVfnT1+Vu9p5GdrDvKnmqNkJMfzzVsv5L3zikbi40XEA5IT4vjby6Zwxzsm8fRfD/HE+lr+7bkavvp8DXOLs7lixngWTc5lTnEWqYmxexw8ZGVmFgd8F7gaqAPWm9kzzrltvZpdB5SHbouB74e+RtzR1g7W7G1kx5E2th9pY83eRk51+clMjucTV0/nrovLyExOGI6PFhGPS4qP49aFpdy6sJRdR9t4bvMR/rLjGP/5p504Bz6DKfnpTM1PoywvjfEZyeSlJzIuLYlx6YlkpyaQGOcjId5HYlzwBtDlD9DtD9DVE6Db70hJiCMrNfI5Fc6fmkXAbufcXgAzewK4Eegd6DcCP3HBjqc1ZpZtZhOcc4cjXXDl/ibuf6KKeJ8xJT+N980r4qqZBSyZOo7khLhIf5yIjFHlBRncX5DB/VeV03Syi6q6Zv56oIntR9rY03CSv2w/Rrf/7Pra7106lU9fOyPCFYcX6EVAba/Hdbz96Lu/NkXAWwLdzFYAK0IP281sxxlV28ce4AXgq+fyJucmDxjtl51pH2KD9mEEfHDoJiOyD5/5d/jM2b980kAbwgn0/oaI9P2zFE4bnHMPAw+H8ZmjgplVOucqol3HudA+xAbtQ2wY7fsQzhCQOqD3NfPFQP1ZtBERkWEUTqCvB8rNbLKZJQLLgGf6tHkGuMOCLgJahqP/XEREBjZkl4tzrsfM/h74A8Fhiyudc1vN7J7Q9oeA5wgOWdxNcNjih4ev5Jjihe4j7UNs0D7EhlG9DzZar4gSEZG30mWUIiIeoUAXEfEIBXoYzOxaM9thZrvN7G3DR0Mng78d2r7JzOZHo87BhLEPHwzVvsnMXjezudGoczBD7UOvdgvNzG9mN41kfeEIZx/MbKmZVZnZVjN7eaRrHEoYP0tZZvasmVWH9iGmzqmZ2UozO2ZmWwbYHvO/zwNyzuk2yI3gieA9wBQgEagGZvVpcz3wPLZbOsMAAAVqSURBVMHx+BcBa6Nd91nswzuAnND960bjPvRq9xeCJ+pvinbdZ/F9yCZ4FXZp6PH4aNd9FvvwWeDfQ/fzgRNAYrRr71XfZcB8YMsA22P693mwm47Qh/bm1AfOuS7gjakPentz6gPn3Bog28wmjHShgxhyH5xzrzvnmkIP1xC8liCWhPN9APhfwFPAsZEsLkzh7MNtwK+dcwcBnHOxth/h7IMDMkKT9qUTDPSekS1zYM65VwjWNJBY/30ekAJ9aANNa3CmbaLpTOv7KMEjlFgy5D6YWRHwPuChEazrTITzfZgO5JjZS2a2wczuGLHqwhPOPjwIzCR4ceFm4H7nXGBkyouIWP99HlDszgMZOyI29UEUhV2fmb2TYKBfMqwVnblw9uGbwKedc/4YXdQknH2IBxYAVwIpwGozW+Oc2zncxYUpnH24BqgCrgCmAi+Y2SrnXOtwFxchsf77PCAF+tC8MPVBWPWZ2Rzgh8B1zrlYW48rnH2oAJ4IhXkecL2Z9Tjnnh6ZEocU7s/ScefcSeCkmb0CzAViJdDD2YcPAw+4YIf0bjPbB8wA1o1Miecs1n+fB6Qul6F5YeqDIffBzEqBXwMfiqGjwd6G3Afn3GTnXJlzrgz4FfCxGApzCO9n6b+BS80s3sxSCc5sWjPCdQ4mnH04SPB/GJhZAXAesHdEqzw3sf77PCAdoQ/BeWDqgzD34XPAOOB7oSPcHhdDs86FuQ8xLZx9cM7VmNnvgU1AgOAKYf0Or4uGML8PXwYeNbPNBLsvPu2ci5lpdc3s58BSIM/M6oDPAwkwOn6fB6NL/0VEPEJdLiIiHqFAFxHxCAW6iIhHKNBFRDxCgS4i4hEKdBERj1Cgy6hgZu39PPcFMzsUmmp2l5n92sxm9Wkzz8ycmV3T53l/6HVbzOyXoYt4MLN/CU35uim0ffEgNSWY2QOhz95iZuvM7LrQtnQz+y8z2xN6v1fMbLGZlZjZi2ZWE3r+/sj8C4nowiIZ/f7TOfd1ADO7FfiLmV3gnGsIbV8OvBr6+oderzvtnLsw9LrHgHvMbDVwAzDfOddpZnkEp4gdyJeBCcDsUPsC4PLQth8C+4By51zAzKYQnLDqIPBJ59xGM8sANpjZC865bef8LyFjngJdPMM59wsz+xuCU9B+KzR9603A1cAqM0t2znX089JVwBxgP8F5VDpD7zfg1Y2hI/q/BSb3an8UeNLMphK8ZP+Db8wy6Jzby/9c/n449FybmdUQnMlPgS7nTF0u4jUbCU4EBXAxsM85twd4ieDl3G9hZvEEF/TYDPwRKDGznWb2PTO7vG/7XqYBBweYQfB8oMo55x+sUDMrA+YBawdrJxIuBbp4Te+pT5cTXICB0NflvbalmFkVUEmwG+QR51w7walrVwANwC/M7K5hKdIsneBCHB8fRdPKSoxTl4t4zTyg0szigA8A7zGzfyEY9OPMLMM510avPvTeQkfVLwEvhSaXuhN4tJ/P2Q2U9nq/3rYCc83M19/CDmaWQDDMH3PO/fpsd1SkLx2hi2eY2QeAdwE/B64Cqp1zJaEpdScRDNH3DvL688ysvNdTFwIH+mvrnDsFPAJ8OzSNLGY2wcxuD3XxVAJfDPXjY2blZnZj6PEjQI1z7hvnus8ivSnQZbRINbO6XrdPhJ7/xzeGLQK3A1eERrgsB37T5z2eInjCdCDpwI/NbJuZbQJmAV8YpP2/Euya2WbBFeSfDj0GuBsoJLjAw2bgBwQXSbgY+BBwRajuKjN7W9++yNnQ9LkiIh6hI3QREY/QSVGRIZjZb4DJfZ7+tHPuD/21F4kWdbmIiHiEulxERDxCgS4i4hEKdBERj1Cgi4h4xP8HvAIQN0XQps4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e0829ee50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e080a6bb0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07fa6190>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07fcd070>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07e5ef40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e081bc310>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07e83370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e0858fa00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e085aa0d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e086f1ac0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e08737c10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e08002fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e07e4ff10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"# 生成整体数据概要\\ndata_report=pp.ProfileReport(Data) \\ndata_report\\n\\n# 也可以把生成的报告导出保存\\ndata_report.to_file('report.html') \\n\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''# 生成整体数据概要\n",
    "data_report=pp.ProfileReport(Data) \n",
    "data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Present_Tmax      7682 non-null   float64\n",
      " 1   LDAPS_RHmax       7677 non-null   float64\n",
      " 2   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 3   LDAPS_WS          7677 non-null   float64\n",
      " 4   LDAPS_LH          7677 non-null   float64\n",
      " 5   LDAPS_CC1         7677 non-null   float64\n",
      " 6   LDAPS_CC2         7677 non-null   float64\n",
      " 7   LDAPS_CC3         7677 non-null   float64\n",
      " 8   LDAPS_CC4         7677 non-null   float64\n",
      " 9   LDAPS_PPT1        7677 non-null   float64\n",
      " 10  LDAPS_PPT4        7677 non-null   float64\n",
      " 11  lat               7752 non-null   float64\n",
      " 12  lon               7752 non-null   float64\n",
      " 13  DEM               7752 non-null   float64\n",
      " 14  Slope             7752 non-null   float64\n",
      " 15  Solar radiation   7752 non-null   float64\n",
      " 16  Next_Tmax         7725 non-null   float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "dic = dict(zip(Max.mean().index,Max.mean()))    #是否应该先划分数据集 再填充缺失值\n",
    "Max = Max.fillna(dic) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
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       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
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       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
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       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "      <td>-0.632499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "      <td>-0.536418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "      <td>-0.792634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>2.762374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205   0.296287  \n",
       "...        ...       ...       ...       ...              ...        ...  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  -0.632499  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  -0.536418  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  -0.792634  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935   2.762374  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "#标准化处理连续型特征 （10分）\n",
    "Scaled_Data = pd.DataFrame(StandardScaler().fit_transform(Max), columns=Max.columns)\n",
    "Scaled_Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.3,random_state=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression().fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.75528075712334, 0.7520421552073728)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(0.7570395775105323, 0.7428694968905715)\n",
    "lr.score(x_train, y_train), lr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:  0.24843292023773117\n",
      "RMSE:  0.49843045677178593\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "y_pred = lr.predict(x_test)\n",
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('RMSE: ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e0819ef10>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e08179400>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25e081795e0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25e08481790>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e08481d60>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-TEST')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-PREDICTED')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.plot([-4, 3], [-4, 3], 'k--')\n",
    "plt.xlabel('Y-TEST')\n",
    "plt.ylabel('Y-PREDICTED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25e0826a1c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x25e0826a520>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-TEST')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "\n",
    "plt.scatter(y_test, y_test-y_pred)\n",
    "plt.axhline(y=0, color='b', lw=5)\n",
    "plt.xlabel('Y-TEST')\n",
    "plt.ylabel('Residuals')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o0aOvKZ+bmxtPP/0006dPZ9y4cTf4lJeULVuWxx57jIiICO6++24aNmwIQHJyMh9++CGzZs2icuXK9OjRg7Fjx/Lee+/d9JwiIiIiIreVXC2VtMdg1Rd03bYC7w4u7giF2nhiVXFHEBEREZHbUNav84p1frcHuxbr/IVx+KWSIiIiIiJyB9HmJHbdsYVbv379bDs9/mXIkCEEBWnbexERERERcSx3bOE2efLk4o4gIiIiIiJyTe7Ywk1ERERERByQNiexy1jcAUREREREROTK1HETERERERHHoY6bXeq4iYiIiIiIODgVbiIiIiIiIg5OSyVFRERERMRhWK2W4o7gkNRxExERERERcXDquImIiIiIiOPQ5iR2qeMmIiIiIiLi4NRxu43F9i5f3BHs8v+/I/h6PVDcMezak7S5uCOIiIiIiFw3FW4iIiIiIuI4rFoqaY+WSoqIiIiIiDg4ddxERERERMRxaHMSu9RxExERERERcXAq3ERERERERK7DunXrCA0NpX379nzxxRcFrl+4cIEXXniBjh07Eh4ezo8//njTc2qppIiIiIiIOA4H35zEYrEwevRoZs6cSaVKlYiMjCQ4OJg6derYxnzzzTfUrl2bqVOnkpyczMMPP0xERAQuLi43PK86biIiIiIiItdo586d1KhRg2rVquHi4kJ4eDgrV67MN8ZgMJCeno7VaiU9PZ1y5cpRosTN9cxUuImIiIiIiFyjxMREvL29bceVKlUiMTEx35gePXpw8OBBgoKC6NixI2+++SZG482VXloqKSIiIiIijqOYd5WcN28e8+bNsx137dqVrl272o6tVmuBewwGQ77jDRs24Ovry5dffsnRo0fp06cP/v7+uLu733AuFW4iIiIiIiJ/urxQu5y3tzcJCQm248TERLy8vPKNmT9/Ps899xwGg4EaNWpQtWpV4uPjadSo0Q3n0lJJERERERFxHNbc4v25ioYNG3L48GGOHTtGTk4OMTExBAcH5xtTuXJlfvnlFwDOnDnDoUOHqFq16k29Leq4iYiIiIiIXKMSJUowcuRInnnmGSwWC126dKFu3brMmTMHgG7duvHSSy8xfPhwIiIisFqtDBkyBE9Pz5ua12C1t0hTbgvpbz5W3BHs8v+/I8UdoVB7kjYXdwQRERERuYLMZZ8W6/wlQ/sX6/yFUcdNREREREQcRzFvTuKoVLj9yznVbYJLeB8wGjFvWcnFdQvzXTfWuhe3J4eSey4JAMuuTVxc/QOGClVwfWLgpXEeXuSsnIf555+KNN8bYwfTql0LsjKzeOPl0ez+bV+BMWM+fov7mvhiMMDhg0d5Y8BoMtIzebrfk3To8jAAJZycuMenJoG+oZxPSS3SjCIiIiIixU2F27+ZwYhLRF+yZr6LNTUZtxfHY96zBevp4/mGWQ7vIfurCfnOWc+cJOvT12yvU3Lo51h2F+0yw1ZtW1Djnmo8HNCFxs0aMPL9oTzxyNMFxo0f8THpaekADB39Kt2ffoxpk75kxuSvmTH5awBah7Sk1/PdVbSJiIiI3O7UcbPrqrtK+vn5FTg3adIkgoKCMJlMhISE0L9/fw4cOJBvzO7du6lXrx7r16/Pd97X1xeTyUSHDh0YMGAAmZmZAEyZMoXw8HAiIiIwmUzs2LHDbp5+/fphMplo3749zZo1w2QyYTKZ2LZt2zU/9M06fvw4HTp0+Mfmu1HGqnXITU7Aei4JLGYsOzdSwtf/ul/HqXYDrMkJWFPOFGm+4EdaEfVdXgdvx9bfKVuuDBW9yhcY91fRBuDm5mr3tcIfDeWnBcuKNJ+IiIiIiKO44Y5b79696du3LwA//fQTvXr1Ijo62rZbyuLFi2nWrBkxMTEEBQXZ7nNzcyMqKgqAwYMHM3fuXJo0acKaNWtYsGABLi4uJCcnc/HiRbvzTp48GYBNmzYxY8YMPv/88xt9hH89Q1lPrOfP2o6tqckYq9UtMM6pug9u/T/AeuEcOUu+xJqUvyPn1CgQ886NRZ6vkrcXCScvfct8wskkvCp7cTrpbIGxY/8zglbtWnBw3yHee/uTfNfcSrrSMvhBxgz/oMgzioiIiIg4giL5HrewsDACAwOJjo4G8r5NfNmyZUyYMIENGzaQnZ1t9z5/f3+OHDnC6dOn8fDwwMXFBQBPT08qVap03TmCg4P56KOP6Nq1K507d2bXrl307duXdu3a2bbnTE9Pp1evXjz66KNERESwYsUKAHbu3ElERATZ2dlkZGQQHh7O/v37rzrn8ePH6d69O48++iiPPvqorfO3adMmevToQb9+/QgLC2PkyJHk5uZisVgYNmwYHTp0ICIiglmzZgFw9OhR+vbtS+fOnenevTsHDx687ucvwGDn3GWbiOaePETGBy+R9elrmH9ZgluP1/OPdypBifr+mH/75ebzXB7PTr7CNjl985V3eahhOPF/HOYRU/t819qEBBG3eaeWSYqIiIj8Gzj497gVlyL7jNu9995LfHw8AFu3bqVq1apUr16dgIAA1q5dS0hISL7xZrOZdevWERQURGBgIJMnTyY0NJTmzZsTFhbGAw88cEM5vL29mTdvHuPGjWPYsGHMmTOHnJwcwsPD6datG66urkyePBl3d3eSk5Pp2rUrbdu2pVGjRgQHB/PJJ5+QlZVFx44d8fHxuep85cuXZ+bMmbi6unL48GEGDRrE/Pnzgbxi8KeffqJKlSo888wzxMbGUrVqVRITE1m8eDEAqal5xcaIESN45513qFmzJjt27OCdd97hyy+/vKH34C/W88kYyl1aemgo64k1NTn/oOxM26+W/XHQ8RkoVQYyLgDg5NOE3JOHIP38TWX5S/enI4l8shMAv8ftxrvKpQLdu4oXpxNOF3pvbm4uSxYu5+l+PVkwd7HtfNijIcQsiC2SfCIiIiIijuiWbE4SExNDeHg4kNeNi4qKshVuWVlZmEwmIK/jFhkZiYuLC/Pnz2fLli1s2rSJgQMHMnjwYDp37nzdc7dt2xYAHx8fMjIycHd3B8DV1ZXU1FRKlizJRx99xP/+9z+MRiOJiYmcOXOGihUr0q9fPyIjI3F1deWtt966pvnMZjOjR49m7969GI1GDh8+bLvWqFEjqlWrBkB4eDhbt26lefPmHDt2jHfffZeHHnqIli1bkp6eTlxcHK+88ort3pycnOt+9svlnjiAsXxlDB5eWFOTcWoUSPZ3/8k3xuB+F9a0FCDvM3EYjLaiDaBEo5aYd2646Sx/+XbGD3w74wcAHmoXSPe+j/HTglgaN2vAhdQ0u8skq9eqytFDecs3W4cGEX/gsO2ae5nS+Df34/WXRhZZRhEREREpRtqcxK4iK9x2795NgwYNsFgsxMbGsmrVKqZOnYrVaiUlJYW0tDTc3d3zfcbt75ycnAgICCAgIAAfHx8WLlx4Q4Wbs7MzAEaj0bb08q9js9lMdHQ0ycnJzJ8/H2dnZ4KDg21LOc+fP09GRgZms5ns7GxKlSp11flmzZpFhQoViIqKIjc3l0aNGtmuGS5bC2gwGChXrhxRUVFs2LCBb7/9liVLlvDmm29StmxZu+/LTcnNJSd6Om693wSDEfO21ViTjlPigbylhubNy3Fq8CDOD4RgzbXAxRyy53186X5nF5zqNCJ74RdFm+tPa1dspFW7FizbPJ+sjCzeeOVd27XPv/2YtwaO5UzSWcZPeht399IYDAb27v6Dd157zzauXVhrfl6zicyMrFuSUURERETEERRJ4bZs2TI2btzIsGHD+Pnnn6lfvz7Tp0+3XR86dCgrVqygU6dOdu+Pj4/HaDRSs2ZNAPbs2UOVKlWKIloBFy5coHz58jg7O/Prr79y4sQJ27URI0bwyiuvcPz4cSZOnMjIkVfv4ly4cAFvb2+MRiMLFizAYrHYru3cuZNjx45x9913s2TJEh5//HGSk5NxcXEhNDSU6tWrM2zYMNzd3alatSpLlizhkUcewWq1sm/fPurXr3/Tz2vZH0fm/rh858ybl1/6/delmH9dav/mizlkjC24PX9ReneY/Q1Fnu9+6TvkenR4ttD7F86LYeG8mCLPJSIiIiLiSK5auGVmZtKqVSvbcZ8+fYC8TtOiRYvIzMykbt26zJ49G09PT2JiYmjXrl2+1wgJCWHOnDmFFm4ZGRmMGTOG1NRUnJycqFGjBqNHj76Z5ypUREQEL774Ip07d8bX15d77rkHgIULF1KiRAkiIiKwWCw88cQT/PLLLzRv3vyKr9e9e3defvllli5dSkBAQL4uXZMmTfjwww/Zv38//v7+tG/fnv379zN8+HBy/2wBDxo0CIAPPviAUaNGMWXKFMxmM2FhYUVSuImIiIiI3FYceIOQ4mSwFraNn9yUf+LrCtLffOyWvfbN8P+/I8UdoVB7kor2S8RFREREpGhlRr1frPOXNL1+9UHF4JZsTiIiIiIiInJDtDmJXQ5duPXr14/jx/N/GfSQIUPyfaH3rbRv3z5efz1/xe3i4sL3339/1Xv/2mhFRERERETkZjl04TZ58uRinb9evXpFv9OjiIiIiIjIdXLowk1ERERERO4w2pzELmNxBxAREREREZErU8dNREREREQchzYnsUsdNxEREREREQenwk1ERERERMTBaamkiIiIiIg4Di2VtEsdNxEREREREQenwk1ERERERMTBaamkiIiIiIg4Dqu1uBM4JHXcREREREREHJw6brex174xFHcEu9431i7uCHaNc0qg+d1tijuGXb+cWF3cEUREREQcgzYnsUsdNxEREREREQenwk1ERERERMTBaamkiIiIiIg4Di2VtEsdNxEREREREQenjpuIiIiIiDgOqzpu9qjjJiIiIiIi4uBUuImIiIiIiDg4LZUUERERERHHoc1J7FLHTURERERExMGp4yYiIiIiIo7Dai3uBA5JHTcREREREREHp8JNRERERETEwWmppIiIiIiIOA5tTmKXOm4iIiIiIiIOTh23O8Bjb/fhvjZ+XMzM5sshn3Fs16ECYx56KpQ2T4fjVdOb1/z6kn7uAgD3m1oS8oIJgOyMLOa8NY0Te44USa6KbRrT4N2nMDgZOfrNag58uijfdfc6VWj8yfOUa1iLvRPmET8lJv8LGA20WjaOrIRkNvf8oEgy/WXg6JdpERxAVmYW7w58j/2//1FgzBsTX6N+43oYgKOHjjPm1QlkZmRRo3Y13vx4KPUa1OXz96bz7effFWk2ERERkX81ddzs+ld13Pz8/AqcmzRpEkFBQZhMJkJCQujfvz8HDhzIN2b37t3Uq1eP9evX5zvv6+uLyWSiQ4cODBgwgMzMTACmTJlCeHg4ERERmEwmduzYYTfPihUreOmll2zHn3/+Oe3bt7cdr1q1ihdeeAGAH374gYiICCIiIujQoQMrVqy4sTfhMve19sOrljejWg/gmze+4Imxz9gdd3DrPv775LucPZ6U7/zZY0l81HUUYx95jZ8m/Uj38c8VSS6MBhqO78Om7u+xutUQqjzaAnefu/MNyUlJ4/e3ZhM/ZbHdl7jn2Ue48MeJosnzN82DA6hW624ea/kkE4Z+yOvjB9od98moyTzV/hl6tn+GxBNJRPZ5FIDUlAt8PGKSCjYRERERKTL/qsKtML179yYqKorY2FjCwsLo1asXycnJtuuLFy+mWbNmxMTk7+i4ubkRFRXF4sWLcXZ2Zu7cucTFxbFmzRoWLFhAdHQ0M2fOxNvb2+68TZs2Zfv27bbjuLg43N3dOXv2rO24adOmJCQkMHXqVL799luio6OZN28e9erVK5JnbxTiz6b56wA4HPcHpcqUpmzFuwqMO77rMMnHTxc4H79tP5mp6QAc2vYHHt7liySXh18d0g8lkHE0CetFCycX/oJ3qH++MTlnUjm/PZ5cs6XA/W6VPfFq58fRb1YXSZ6/axUayJIfYgHYtW0P7uVKU97Ls8C4jLQM2++ubi5Y/9y69tzZFPbs2If5ornIs4mIiIjInemOKNz+LiwsjMDAQKKjowGwWq0sW7aMCRMmsGHDBrKzs+3e5+/vz5EjRzh9+jQeHh64uLgA4OnpSaVKleze4+npSZkyZThyJG9pYVJSEiEhIcTFxQGXCrezZ89SunRpSpUqBUDp0qWpVq1akTzvXYv5lO4AACAASURBVJU8OXfyjO34XMJZ7vIuWIRci8CuwexaE1ckudwqe5B58qztOOvUWdwqe1zz/fe9+xR73v0WrEXfSq/oXYHEk5c6j6dPnaGidwW7Y9/86HVitv9IjTrV+X7GgiLPIiIiInLHseYW74+DuuMKN4B7772X+Ph4ALZu3UrVqlWpXr06AQEBrF27tsB4s9nMunXr8PHxITAwkFOnThEaGsqoUaPYvHnzFefy8/MjLi6O+Ph4atSoQZMmTdi2bRtms5l9+/bRsGFD6tevT4UKFWjbti3Dhw9n1apVRfasBoOhwDnrDXypoU/z+2jRtQ0LJ3xTFLHATi6uMZZXe7+8btzOgp/VKxJ23zP7Q8cOep+Ipo9x+I+jtOvY5tbkEREREZE73h2/OUlMTAzh4eFAXjcuKiqKkJAQALKysjCZ8jbm8Pf3JzIyEhcXF+bPn8+WLVvYtGkTAwcOZPDgwXTu3Nnu6zdt2pRt27ZhsVho0qQJDRs2ZPLkyezevZtatWrh6uoKwLRp0/jtt9/45ZdfGD9+PLt27eLll1++oWdq1TOUwG5tATiy4yAeVSoA+wDw8C7P+cRz1/V6d9evTo8JzzO593jSU9JuKNPlsk4mU7LKpWWXbpXLk5Vwbbk8769HpZCmeLVtgtHVGWf3kvh92o+4/pNvOE+XXp3o2CPv38Ge7XupVMXLdq1i5QqcSTxT2K3k5uayctFqerzYlZjvlt5wBhERERGRwtyRhdvu3btp0KABFouF2NhYVq1axdSpU7FaraSkpJCWloa7u7vtM26Xc3JyIiAggICAAHx8fFi4cGGhhZufnx9fffUVubm5PP7447i7u5Odnc3mzZtp2rSpbZzBYKBRo0Y0atSIFi1a8MYbb9xw4bbuq2Ws+2oZAA3a+PFQr4fZsmgjNf3qknkhg9TTKdf8Wh5VyvPs1CHMHvgpSYdO3VAee1K2H6T0Pd6UrF6RrFPJVOnUnG0vfXpN9+4dN5e94+YCUL6FL7Vf7HBTRRvAj7MX8uPshQC0aPsgkb07sTxqFfc19SU9NZ2zSckF7qlaswrHD58EoGX75hw5cPSmMoiIiIgIWHOvf3XYneCOK9yWLVvGxo0bGTZsGD///DP169dn+vTptutDhw5lxYoVdOrUye798fHxGI1GatasCcCePXuoUqVKofPVqVOHpKQktmzZwttvvw3k7VY5d+5cXnvtNQASExM5c+YM9913HwB79+694mtej99Xx3Ffm6a8s/a/5GTm8NVrn9muvTRzGN8M/ZzzSedo3fsR2j/fkbIV7+LNpR+wa3Uc3wz7nLABkbh7uNN1TN5ulLlmC+91HH7TuayWXH5/YxYPzhmOwcnIsTlrSNt3nBpPtQPgyJcrcK1YjqBlYylRpiTkWrnn2UdY0+o1zGmZNz3/lfy88ldaBAfw/cavyc7MZsyg92zXPvxyPONfm8jZpGRGfDKc0u6lwGDgwO6DvD/8YwA8K3owc8nnlHYvRW6ula7PRtKtde98m5mIiIiIiFwPg/VGPvDkoOrXr4+X16Ulbn369CEtLY3vvvsOT09PMjMzqVu3LgMHDqROnToMGzaMxo0b061bN9s9K1euZM6cOUybNs32+bS/+/333xkzZgypqak4OTlRo0YNRo8ejadn4Rt+PPfcc1y4cIE5c+YAMH/+fIYPH8769evx8vLixIkTDB8+nKSkJFxdXfH09OSdd96hevXqV3zel2o+fiNv0y33SJZTcUewa5xTQnFHKNQvJ4p+d0wRERGR21HG1FeKdf5SL/ynWOcvzL+qcLvTqHC7PircRERERByfCjf77shdJUVERERERG4nd9xn3G6Vfv36cfz48XznhgwZQlBQUDElEhERERG5DTnwd6kVJxVuRWTy5Jvb1VBERERERKQwKtxERERERMRx6OsA7NJn3ERERERERBycCjcREREREREHp6WSIiIiIiLiOHK1OYk96riJiIiIiIg4OHXcRERERETEcajjZpc6biIiIiIiIg5OhZuIiIiIiIiD01JJERERERFxHFZ9j5s96riJiIiIiIg4OHXcbmOPZDkVdwS77nbOKO4IdvW23l3cEezKMMLH1Z8s7hiFGnj06+KOICIiIncSbU5ilzpuIiIiIiIiDk6Fm4iIiIiIiIPTUkkREREREXEcudqcxB513ERERERERBycOm4iIiIiIuI4rNqcxB513ERERERERBycCjcREREREREHp6WSIiIiIiLiOLQ5iV3quImIiIiIiDg4FW4iIiIiIiIOTkslRURERETEYVhztaukPeq4iYiIiIiIXId169YRGhpK+/bt+eKLL+yO2bRpEyaTifDwcJ588smbnlMdNxERERERcRwOvjmJxWJh9OjRzJw5k0qVKhEZGUlwcDB16tSxjUlNTeWdd95h2rRpVKlShbNnz970vOq4iYiIiIiIXKOdO3dSo0YNqlWrhouLC+Hh4axcuTLfmOjoaNq3b0+VKlUAKF++/E3Pq47bv1zFNo1p8O5TGJyMHP1mNQc+XZTvunudKjT+5HnKNazF3gnziJ8Sk/8FjAZaLRtHVkIym3t+UKTZyrb2o+qoZ8HJyNk5y0n87Md81z06PYT3S50BsKRnceyNKWTuOfy3bEbqx3zIxYSzHOwz5qayVGvdiBbv9MTgZGTvnDVsnxxdYEyL0T2pHtwEc2Y2awZ+wZnfL2UxGA10/uld0hPOsbT3hwCUv7c6QROexsnVGavZwvo3Z3F6e/xVs9R4qBGtR/XE6GTk97lr+N9nBbO0fqcntdo04WJmNrGDvyDpzyxXurdJ7/Y07hWC1WLh0KrtrB83l/qdWtDs+XDbmIq+1fgm7C1O7z56rW+diIiIyB0lMTERb29v23GlSpXYuXNnvjGHDx/GbDbTs2dP0tPTeeqpp+jUqdNNzavC7d/MaKDh+D78+vg4Mk+dJWjpWBJit5K2/4RtSE5KGr+/NZvKD/vbfYl7nn2EC3+cwLlMySLOZqTamOf5o/vbXDx1lnqLJ3J++Way/jh2KduxRPY/9gaW8+mUbd2U6u/1Y1/H12zXvfp2IOvAMZzcS91UFIPRQOCYXsR0n0D6qWQ6x4zmcOxWUv44aRtTLbgx5Wp5M7flYLya1qbl+N4sjBhlu96g78OcO3ASF/dL71PAm93Y+vF8jq3eSbXgxjz4ZjeiHxt71SzBY3oxv8cELpxKpnv0aA4u30ry37LUbNOYu2p6M7PVYLz9ahM8tjdzTaOueG/V5r7UDmnG16HDseSYKVm+LAB7F/7M3oU/A1C+XlVM0wepaBMREZHiZS3ezUnmzZvHvHnzbMddu3ala9eutmOrteBSToPBkO/YYrGwa9cuZs2aRVZWFk888QSNGzemVq1aN5zrX7tU0s/Pr8C5SZMmERQUhMlkIiQkhP79+3PgwIF8Y3bv3k29evVYv359vvO+vr6YTCY6dOjAgAEDyMzMBGDKlCmEh4cTERGByWRix44dhWbq2bMnv/32W75zmzZt4vnnn893btiwYSxduvS6ntceD786pB9KIONoEtaLFk4u/AXv0PwFWs6ZVM5vjyfXbClwv1tlT7za+XH0m9U3neVypZvUJftwAjlHE7FeNHNu0XrKhTyQb0z61r1Yzqfn/R63D+fKl1rMzt7lKRvsz5k5y286i1eT2qQeTuTC0dPkXrRwIOpXaoY0yzemZkgz9v+wAYCkbQdxLVuaUl535T1LZU9qtG3C3m/X5H9hq9VWyLmUKUV64rmrZvFuUpuUw4mc/zPLvuhfqX1ZltohzdjzY16WhLi8LKW97rrivY17tuN/n0VjyTEDkHk2tcDc9U0t2Bv1y1UzioiIiPybde3alfnz59t+/l60AXh7e5OQkGA7TkxMxMvLq8CYoKAgSpUqhaenJ/7+/uzdu/emcv1rC7fC9O7dm6ioKGJjYwkLC6NXr14kJyfbri9evJhmzZoRE5N/yaCbmxtRUVEsXrwYZ2dn5s6dS1xcHGvWrGHBggVER0czc+bMfG3T4uZW2YPMk5c+CJl16ixulT2u+f773n2KPe9+e0v+q4ezd3lyTp6xHV88dRZn78LX/pZ/oj2pq7fZjquOeoYT42YXyYdXS1X2IO3UpX8D6QnJlL7sfSrt7UH6397L9FPJlPLOG9Ni1JP8OnZOgf/68vOorwl4qxs9Nv+H5iO6sXn8PK7G3duDCycvZUk7lYx7JY+CY05dypKWkIy7t8cV772rljd3P1CPJ6JG8dh3b1Kp0T0F5vaJCGCfCjcREREpbrnW4v25ioYNG3L48GGOHTtGTk4OMTExBAcH5xvTtm1btmzZgtlsJjMzk507d1K7du2belvuuMLt78LCwggMDCQ6Ou9zQFarlWXLljFhwgQ2bNhAdna23fv8/f05cuQIp0+fxsPDAxcXFwA8PT2pVKnSP5b/qi5r2QJwjXWOV3u/vG7czkNFm+kvdqJhp+0M4N68IRW6tssr1ICybf0xn00h87eDRRTlGt4nu++lleptm5B5JpUzvx0ucPnep9ryyzvf8M0Dr/DzqG94aOKz1xCm4DwF3xZ7Y6xXvNdYwohrudLMNY1i3dg5hH/WP9847ya1MWfmcHb/8atnFBEREbmDlShRgpEjR/LMM88QFhbGI488Qt26dZkzZw5z5swBoHbt2gQFBdGxY0cee+wxIiMj8fHxubl5iyL87ezee+8lPj5vw4itW7dStWpVqlevTkBAAGvXriUkJCTfeLPZzLp16wgKCiIwMJDJkycTGhpK8+bNCQsL44EHHrA3zRVt2bIFk8lkOz516hStW7e+qecCyDqZTMkql7pYbpXLk5Vw9eV6AJ7316NSSFO82jbB6OqMs3tJ/D7tR1z/yTedC/I6bC5VKtiOnSuX52JicoFxJevXoMYH/TjQczSWlAsAuPv7Uq79A5Rt0wyjqwtOZUpR8z8DOfzKxzeUJf1UMu6VPW3Hpb09Sb/sfUo/lUzpv72XpSt7kpGYwj3hD1AjpCnVgxvj5OqMc5mSBP/3RVYNmIJPZBA/j/wKgPjFm3jog2eumiXtVDJlqlzK4l7Zk/Sk/FnSEpIp87dlo+7enqQnpuDkXKLQe9NOnePAki0AJO6Ix2q1UtKzDJnJee9pvY4PapmkiIiIyDV66KGHeOihh/Kd69atW77jZ555hmeeufrff9fqju64XS4mJobw8Lwd9sLCwli8eLHtWlZWFiaTiS5dulClShUiIyMpXbo08+fPZ/To0Xh6ejJw4EDmz59/3fP6+/sTFRVl+7m81XqjUrYfpPQ93pSsXhGDsxNVOjUnIXbrNd27d9xcVjTtz8r7B7Dthf9yZuOuIivaANJ3/IFrzcq4VPPC4FwCj45BnF++Od8Y5yoVqPV/wzn8yidkH7q0OcfJ977i9wf6sqvFcxzqN5ELG3fecNEGkLQjnnK1vClTrSJGZyfqmB7kyPJt+cYcid2GT2RLALya1ibnQgYZSSlsnvAd39w/gG+bD2RFv8mc3LibVQOmAJCReI7KzX0BuDvwPs4fSuBqEnbE41HLm7J/ZqkX8SDxl2WJX74N3y55Wbz98rKkJ6Vc8d6DsVuo1uJeIG/ZpJNzCVvRhsFA3fAA9kercBMREREHkJtbvD8O6o7vuO3evZsGDRpgsViIjY1l1apVTJ06FavVSkpKCmlpabi7u9s+43Y5JycnAgICCAgIwMfHh4ULF9K5c+dieJKCrJZcfn9jFg/OGY7BycixOWtI23ecGk+1A+DIlytwrViOoGVjKVGmJORauefZR1jT6jXMaZm3Npwll2MjvqDO16MwOBk5O28lWfuPUeHJhwE48/VSKr/6BCXuKkO1sc/bnmdf+OAij2K15LJhxGzCvnkdg9HIvnlrObf/BL5P5hXQe75exdFV26ke3JgnNnyIOSuHNYO+uOrrrnt9Oi3e6YmxhBFz9kXWDZ1+TVlWjZhN569ex+BkZNe8tZzdf4JGf2bZ+fUqDq3aTs02jemz/kPMmTnEDvniivcC/D5vLSEfPEfP5eOx5FhYNuhz25xVA+qTdiqZ80dPX/d7JyIiIiL/DIPV3n6W/wJ+fn7ExcXlOzdp0iRKlSpF3759AVi2bBmjR48mOjratl3n9OmX/rgeOnQozZs3p1OnTnZfLz4+HqPRSM2aNQH4+OOPuXDhAiNHjrSbqWfPnrz++us0bNjQdm7Tpk3MmDGDzz+/9If0sGHDaN26NQ8//PAVnzHau9sVrxeXu50zijuCXf+zlinuCHZlOHjfe+DRr4s7goiIiNxB0kc+Uazzlx49t1jnL8y/tuOWmZlJq1atbMd9+vQBYNasWSxatIjMzEzq1q3L7Nmz8fT0JCYmhnbt2uV7jZCQEObMmVPol+VlZGQwZswYUlNTcXJyokaNGowePfqKuZ5//nlKlMh725s0aUKPHj1u5jFFREREROQO8K/tuN0J1HG7Puq43Rh13EREROSfpI6bff/ajpuIiIiIiNyGbsF3CP8bqHC7Bfr168fx4/m/D2vIkCEEBQUVUyIREREREbmdqXC7BSZPLrpt80VERERE7ii5+iSXPQ7+6RoRERERERFR4SYiIiIiIuLgtFRSREREREQchjVXm5PYo46biIiIiIiIg1PHTUREREREHIc2J7FLHTcREREREREHp8JNRERERETEwWmppIiIiIiIOA4tlbRLHTcREREREREHp47bbWycU0JxR7Arxt8xt3CNWHOguCPYtbJijeKOUKiJOaV4pmZkccewa9rhH4o7goiIiMg/RoWbiIiIiIg4DqtjNgGKm5ZKioiIiIiIODh13ERERERExHFocxK71HETERERERFxcCrcREREREREHJyWSoqIiIiIiMOwaqmkXeq4iYiIiIiIODh13ERERERExHGo42aXOm4iIiIiIiIOToWbiIiIiIiIg9NSSRERERERcRy5ucWdwCGp4yYiIiIiIuLg1HETERERERHHoc1J7FLHTURERERExMGpcLsDDBz9Mt9v+Jqvlk/Dp0Fdu2PemPgaXy6fxlfLpzH2i1GULOUGQI3a1fhi0aesjV9G9+cfL9Jczn4PUO7Tryj32Te4de5e6DinOvXx+GEVzs0fyn/BaKTsh9Nwf3N8keYCGD1hOBu2LmH5hvk0aOR7xbHvvvcG+4/9r8D5xn4NOHpmJ+EdQ4okU+mgZtRa+gX3LJ+G53OPFbheNqI1NRdNpuaiyVSfOxHX+rVs1zyeMlFr8WfUipmCRy9TkeS5XLe3n2bcmkmMWvIh1e+rZXdMm6ceZtyaSUw7/APuHmVs5+s9eB//3TmbkT99wMifPqDDgMhbklFERETkdqWlkv9yzYMDqFbrbh5r+ST3NfXl9fEDeSbipQLjPhk1mYy0DAAGvP0SkX0e5avJc0hNucDHIybR6uGWRRvMaKTUc69yYdRgcs+epuz7n5OzeSO5x48UHPfU81zcXrAwcusQieX4EQylShVptOD2QdSqXYOWzR6hqX8jxn84koj23eyObdTkPsqVK1PgvNFo5M1Rg1izamPRhDIaqfT2Sxzr8yYXE85Q88dPSFv5KzkHj9mGXDyeyNEnh5KbmkbpVv54vzuAI48NxKVuDe56PJTDkQOxXrxItenvkrbmf1w8crJosgENW/vhVasyb7R+mXv86vLk2OcY12l4gXEHtu5j56qtvDb3nQLX/vjfXib1LfoiXERERG4zWipp123dcfPz8ytwbtKkSQQFBWEymQgJCaF///4cOHAg35jdu3dTr1491q9fn++8r68vJpOJDh06MGDAADIzMwGYMmUK4eHhREREYDKZ2LFjR6GZLl68yMSJEwkJCaFDhw5ERkaydu1aANLT0xk5ciTt2rUjPDycHj162F5r+PDhNG/enA4dOtzUe3K5VqGBLPkhFoBd2/bgXq405b08C4z7q2gDcHVzwWrN+z/MubMp7NmxD/NFc5HmKlHXl9xTJ8hNPAVmMzkbVuHyQMHi0DWsMzm/rMV6/ly+84byFXFu9iDZKxYXaS6A0LBgfpi7CIBtW3ZSrlwZvCpVKDDOaDQyYvQQxrz9YYFrTz/Xg5jo5Zw9nVwkmdwa+ZBz5CQXjyXARTOpMetwb9c835jMuD3kpqbl/b59LyW8ywPgWrsamTv2Yc3KBksuGZt/p0z7FkWS6y9NQu7nl/lrAIiP+4NSZUpRruJdBcYd23WIs8dPF+ncIiIiIneC27pwK0zv3r2JiooiNjaWsLAwevXqRXLypT+gFy9eTLNmzYiJicl3n5ubG1FRUSxevBhnZ2fmzp1LXFwca9asYcGCBURHRzNz5ky8vb0Lnfs///kPp0+fZvHixSxevJipU6eSnp4OwFtvvUW5cuWIjY0lJiaGCRMmcO5cXkHSuXNnpk2bVuTvRUXvCiSeTLIdnz51horeBYsQgDc/ep2Y7T9So051vp+xoMiz/J3BswKWM5dy5Z49jbF8hQJjXB4MInvZogL3l366Pxmzp96S/yLjXdmLkycSbMenTibiXblSgXF9nu1O7JLVJCWeKXD/wx3a8tWMeUWWyblSecwJl+YxJ5zBuVL5QsffFRlC+rqtAGT/cYRS/g0w3lUGg5sr7g/541zZ/r+BG3VXpfIknzxrOz6XkMxd3oXns6d2Ux/eXjKRV2a9SZW6VYs0n4iIiNw+rFZrsf44qn9l4fZ3YWFhBAYGEh0dDeT9Q1i2bBkTJkxgw4YNZGdn273P39+fI0eOcPr0aTw8PHBxcQHA09OTSpUK/hEPkJmZyffff8+IESNs4ytUqEBYWBhHjx5lx44dvPrqqxiNeW97tWrVaN26NQD3338/5cqVK8pHz2MwFDhV2L/HsYPeJ6LpYxz+4yjtOrYp+ixXycVluUr3fZmMLz8v8F0ezv7NyT2fgiV+/y2KZu89yx+ukndFOnQKZcYX3xQY+864YYwb9RG5RfkdJHbfL/v/Q5YKaES5x0JI+mAGADkHj3H2/76n+syxVJv+Lll7D2E1W4ouWyHxCv2HZseR3+MZGvgi7zwyhFWzfqLfF0OLLpyIiIjIv8Ad8Rm3e++9l/j4eAC2bt1K1apVqV69OgEBAaxdu5aQkPybR5jNZtatW0dQUBCBgYFMnjyZ0NBQmjdvTlhYGA888IDdeY4cOULlypVxd3cvcO2PP/7A19cXJyenon/Ay3Tp1YmOPcIB2LN9L5WqeNmuVaxcgTOXdYj+Ljc3l5WLVtPjxa7EfLf0lmW0nj2NU4VLuYzlK5KbnD+XU+16uA8emXe9TDmcmz1IusVCCR9fXO5vgXOzAAzOLhhKlab0q2+S/snYG87T65lu9Hgqb0OM7dt+p8rdl7qqlatUIjEhKd/4Bo18qVmrOhu3LQGgZCk3NmxdQstmj9DI7z4+mz4RAE9PD4LbB2E2m1n206obzncx4Qwl/tYpLeFdgYtJBZdhutariffYVzj2zEhyUy7Yzp//IZbzfy6ZrTCoV77u3Y1q0/Nhgrq1BeDwjoN4VrnUYfPw9iQl8dqXiWalZdp+/21NHD3GOOHuUYa0cxeucJeIiIjIneOOKNz+LiYmhvDwvKImLCyMqKgoW+GWlZWFyZS3456/vz+RkZG4uLgwf/58tmzZwqZNmxg4cCCDBw+mc+fOxfYMV/Pj7IX8OHshAC3aPkhk704sj1rFfU19SU9N56ydP/ir1qzC8cN5m1W0bN+cIweO3tKM5j/2YqxcFaOXN7nJZ3BpGUz6x+/mG3P+hSdsv5d+eRg5W37h4uYNXNy8gcyv/w+AEvc1wa1T15sq2gBmT5vD7GlzAGgb0orez3Yn6sefaOrfiNTUtALLIVfGrsOv/qVdLvcf+x8tmz0CQPMmobbzH08ey4pla2+qaAPI+m0/LjWr4Fy1EhcTz1I2vBUnB72fb0yJyhW5+9O3OPXaRC4ePpHvmpNnOSzJ5ylRuSJlQlpw5PHBN5UHYPVXS1n9VV5x37BNU4J7PcLmRRu5x68umRcyOH865Zpfq2zFu0j9c3ytxnUwGAwq2kRERO5U2pzErjuicNu9ezcNGjTAYrEQGxvLqlWrmDp1KlarlZSUFNLS0nB3d7d9xu1yTk5OBAQEEBAQgI+PDwsXLrRbuNWoUYNTp07ZXu/v6taty969e8nNzbUtlfwn/LzyV1oEB/D9xq/JzsxmzKD3bNc+/HI84/+fvTuPi6re/zj+mhlANhFwA9Rc0dxK1Fyu4paCiYap96a3TbMycy9NzSUlNa9ZWmradtM261bmAm6Ve1mW+5ILKq4IqCAKwzLL7w9qlBjuT4Uuo76fj0ePx8w5nzPnPYe53vnO53vOGTWTC8kXmTB7LD6+3mAwEH/gKDPGzgIgsHwAH656Bx9fb2w2Ow8/3Ys+7frmu5jJTbFZyXxvNqVfnglGI9nfr8R6KoFSkQ8COD2v7X/l+7Wb6NCpDT/sWIXZnMXzg8Y71n30n/mMGjqRpHP/4wtsWG0kxcynygdTwGTk0ldryYk/iX/vLgCkfb6ScoP/icm/NBUn5V011G6xcaLnMAAqzR2Hyd8Pu8VC0uS3HRcxKS571++gYfvGTNs4lxxzNh+OetuxbtiHL7Fw9HwuJadyf98uRA6Ipkx5fyatfp2963ewaMwCmjzQgnaPRmKzWsnJyuHdIbOLNZ+IiIjIrc5gd+Uz8P4fYWFh7Ny5M9+yOXPm4O3tTf/+/QFYs2YNMTExrFixgv3797Nw4UI++OADR/3o0aNp2bIl3bt3d/p6x44dw2g0Uq1aNQBmzZrF5cuXmThxotNMM2bMIDU1lcmTJ+Ph4UFycjJbt24lOjqaYcOGUb16dYYNG4bBYCAhIYH4+Hg6duwIwOnTp3n22WeJjb2+KyW2rPQXn4d2k+KaFeO5XcWo4YaiTw/8K3xfvmpJRyjUzJzivdVCcXo/4auSjiAiIiJ/gfSni+ceuDfL7721Jbr/wtzSHTez2UybNm0cwNhrLgAAIABJREFUz/v16wfAwoULWb58OWazmdDQUBYtWkRgYCBxcXGOQdIfIiIiWLx4Md27d3e6j8zMTKZMmUJ6ejomk4mqVasSExNTaKbhw4cze/ZsoqKiKFWqFF5eXgwdOhSAqVOnMn36dDp16oSXlxf+/v6MGjUKgOeff55t27aRmppKmzZtGDJkCH//e8GbLIuIiIiI3NY0VdKpW7rjdqdTx+3GqON249RxExERkf+19P6dSnT/fh98W6L7L8wt3XETEREREZHbi10dN6c0cLtJgwYN4vTp0/mWjRw5kvDw8BJKJCIiIiIitysN3G7SvHnzSjqCiIiIiIjcITRwExERERER16Gpkk79724oJiIiIiIiIjdFHTcREREREXEdrnmB8hKnjpuIiIiIiIiL08BNRERERETExWmqpIiIiIiIuAzdx805ddxERERERERcnDpuIiIiIiLiOtRxc0odNxERERERERengZuIiIiIiIiL01TJW1iwW+mSjuBUbtqlko7gVEqma+bKzTWVdIRCnbReKekITi1b0Blz3OySjuGUV9Twko4gIiJya9N93JxSx01ERERERMTFqeMmIiIiIiIuQ7cDcE4dNxERERERERengZuIiIiIiIiL01RJERERERFxHbo4iVPquImIiIiIiLg4ddxERERERMRl6OIkzqnjJiIiIiIi4uI0cBMREREREXFxmiopIiIiIiKuQxcncUodNxERERERERengZuIiIiIiIiL01RJERERERFxGXZNlXRKHTcREREREREXp47bHaD/pKdp3L4p2eZs5o6czbF9xwrUDH/zeWo2rIXVYuXI7iMsGDsPq8VKpZqVGDxzGDXq1+SzmR+z7N2lxZbL475mlB48BExGzHFxZC7+LN/6Uq1a4dOvf97PLlYrl+fOJXffXgC8evbEO6orGAyYY2PJ/PqrYssF8MYbMXTu3AFzppn+T41g1659BWref+8Nwtu0IP3SZQCeemoEu/ccoE/vhxg58jkArlzJYMiQsezZ+1uRM/m2aUzIy0+D0UjqF9+SsiD/e/aPbku5Z3sCYMvI4uyEt8n6LQEAY2kfKv9rCKVqVwW7nTMvvknmzkNFznSt5yYP5L4O95Ftzmbm868Tvy++QM2Yt14k9J7aWC0WDu46xJtj3sJqsQJwT4t7GDhpACY3N9JTLzHy7y8WOdMPv51kxtIt2Gx2HmpRlyfvb5xv/cJ1O1m54wgAVpuN40lprI/pSxkfTz7euJtvfvoNgwFCg8syuXd7Srnrn0wREZG/nDpuTulbyG2ucfsmBFcPYVDbAdQOq8MzUwYypvuoAnWblm5k9rA3ABjx1kg69o5gzSeruJJ2hQ9efpdmkS2KN5jRSOlhw0kb9QLWlBQCF7xD9o8/YD1xwlGSs30H2T/8AIBbjRqUeXkSF554HFO16nhHdeXCwGch14L/jBlk/7QV65kzxRKtc+cO1KpVnXr1WtOsWWPmznmV1uHdnNaOHTOVJd/E5Vt2POEk93fsRVraJSIj2/P22zMK3f66GY2ExDzL8ccmYDl3gZrL3iD9u5/Jjj/lKMk5lcSxh8diS8/At20TKk0bzNGHRgIQ8vLTXN64g5PPTcfg7obBs1TR8vzJfe3vo1L1EPqFP8ndYXczdNpghj44vEDd99+sZ/rQGQCMnTuGB/p0JvbjOHz8fBgydRAvPTaelLMp+JctU+RMVpuNV5dsZsGz3ahYxodHZn1N2/rVqBkU6Kjp2yGMvh3CANi4P4FPNu6mjI8nSWlXWLx5L0te7I2nhxujFq1l9c54opvdXeRcIiIiIjfjlp4qGRYWVmDZnDlzCA8PJzo6moiICAYPHkx8fP5f/g8cOECdOnXYvHlzvuV169YlOjqarl27MnToUMxmMwDz588nKiqKbt26ER0dze7duwvNlJuby8yZM4mIiKBr16706tWLjRs3ApCRkcHEiRPp2LEjUVFRPPLII+zevZvExEQee+wxHnjgAaKioli0aFFRD41Ds07N2fD1egAO7zyEj58PARUCCtTtWL/d8fjI7sOUDS4LwKULl4jfE48111psmQDc766L9ewZrImJYLGQtW4dpVq1zldjzzI7Hhs8vcCe99italVyDxyA7GywWcndvZtS4W2KLVu3bhF8+kleN2vbth34+/sRFFThurf/6aftpKVdAuDnn3dQqVJwkTN53xtKzolEck8lYc+1cGnFJvw6Nc9Xk7njILb0jLzHOw/iHlQOAKOvFz7NGpD6xVoA7LkWbJczipzpWn+LaMm3X38PwMGdB/Hx8yWwQmCBul/W/+J4fGjXIcoF52Xs0L09P6z+kZSzKQCkXbhU5Ez7TiZTpVwZKpf1w93NRGRYLTbsSyi0ftWOI3QOC3U8t9psZOdasFhtZOVaKF/Gp8iZRERERG7Wbdlx69u3L/379wdg5cqVPPHEE6xYsYLAwLwvkrGxsTRp0oS4uDjCw8Md23l6erJs2TIAXnjhBT7//HMaNWrEhg0b+Oabb/Dw8ODixYvk5uYWuu8333yTlJQUYmNj8fDw4Pz582zbtg2A8ePHU7lyZdauXYvRaOTUqVMcPXqU4OBgxowZQ/369bly5Qo9e/akVatW1KpVq8jHIjCoLOd//zIMcOHcBQIrliU1OdVpvcnNRLse7flg0ntF3vd/YyxXDltysuO5LSUF97p1C9SVah2O79NPY/QPIG3sGAAsx4/j2/8pDH5+2LOz8WjeAsuh4pv2FxISxKnTZx3PT59JJCQkiHPnkgvUxsS8yLhxw1m3fgvjxr1KTk5OvvX9+vVmzZr1Rc7kFlSW3MTzjue55y7g3ah2ofWBD0dweWPeYNyjShCWi5eo/NpwPOtWw7zvKGcnv4vdnF3kXH8oG1TWMegCOJ+YQtmgslxMvui03uRm4v4e9zN/0nwAKlWvhJu7G6/9ZwbePl588++lfPf7QPBmJV/KIMj/6mCror8Pe08U/BsCmHNy+fHgKcb2CP+91pfH2zWi8ysf4+nuRos6VfhbnSpFyiMiIiLXRxcnce6W7rhdjy5dutCqVStWrFgBgN1uZ82aNUyfPp0tW7aQne38y2vTpk05ceIEKSkpBAQE4OHhAUBgYCAVK1Z0uo3ZbObLL79kwoQJjvpy5crRpUsXTp48ye7duxk+fDhGY95hr1KlCu3ataNChQrUr18fAF9fX2rUqEFSUlKxvH+DoeAyu91eaP0zU57lwM/7+e2XA8Wy/0I5DVZwUfaWzVx44nHSJozD58knAbCePEHG558R8NrrBPzrNSxH47FbLcUYrWA2Z8ds/ITpNGjYlpZ/iyIwwJ9Rv5/X9oe2bf9Gv769eWnc1OIIdV2ZAHxaNCTgH504N31h3qZuJrzq1+TCpyuJ7zocW2YWFQb2KnqmfPGcftAKrR8ydTB7f97Lvm37gbyBXGjDWkx4YgJjHx3HI8P+SaXqlYqUydnuncUE2LT/BI2qB1HGxxOA9MxsNuw7Ttz4R1k76XHMObnE/Xq4SHlEREREiuK2H7gB1KtXj2PH8i7IsX37dipXrsxdd91F8+bNHdMYr2WxWNi0aRO1a9emVatWJCYmEhkZyaRJkxzdM2dOnDhBcHAwvr6+BdYdOXKEunXrYjKZ/mvW06dP89tvv3Hvvffe4Lu8qvPjXXh95WxeXzmbi0kXKRdS3rGubFBZUgvpgvxjWG/8Asvw4Ssf3PS+r5ctJQVjhavTD43ly2O9cL7Q+tw9e3ALqYTBL+/cp6yVK7k44GlShw/Ffvlykc9ve/bZJ/hl2xp+2baGxLNJVKkc4lhXuVIwiYkFB9J/dOBycnJY9NF/aHpfI8e6hg3qsmDBDHr2epKLF9OKlA3Aknge99+nFQK4B5XFklTw7+h5dzUqTR/CiWemYE3Lu2hKbuJ5cs+dx7wrb+BxadUPeNavWeRM3Z7oxvzV85i/eh4Xki5Q/prPWbng8lxwkg/g0eGP4F+2DO/EvOtYdj7xPL9u2E6WOZv01HT2/ryPGvVqFClfRX8fzqVdnRKalJZBeT/n0x1X74ync9jVDvdPh09TKdCPQF8v3E0m7m9Yg10J54qUR0RERK6TrYT/uw6bNm0iMjKSTp068e677xZat2fPHurWrcvq1auv990X6o4YuF0rLi6OqKgoIK8bFxsb61iXlZVFdHQ0PXv2JCQkhF69euHj48OSJUuIiYkhMDCQESNGsGTJkr8kW0ZGBkOHDuWll15yOvi7Xqs/WskLXYbzQpfhbFv7M+16tgegdlgdMi9nOp0m2bF3Jxq1DWPWkJn/tSNXXHIPHsRUqTLGoCBwc8OzQweyf/whX40p5GrHxS00FNzcsKfnnftk8PcHwFihAqXCw8n6/rsi5VmwYBH3NYvkvmaRLF+xmkcezetINWvWmEuXLjudJnnteW8PPhjJgf150zWrVAnhi/+8R79+wzhy5HiRcv0hc88RSlULwb1yRQzubpTp1ob07/L/iOAeUp675o/l9PNvkHP86lRPy/k0chPP41Ej73j6/u3efBc1uVkrFq1gYOdBDOw8iB/XbKVTz/sBuDvsbjIuZzidJtm5d2eatG3CtMHT833Ofly7lQbN6mM0GSnlWYq7w+pwKv5kkfLVr1KBkylpnLmQTq7Fypqd8bRtUK1A3WVzNtuPnqV9g+qOZcEBvuw5kYQ5Jxe73c7PR05To2LBc0NFRETkzmO1WomJieH9998nLi6O2NjYAtfU+KNu5syZtG7d2smr3Ljb8hy3Pztw4AANGjTAarWydu1a1q1bx4IFC7Db7aSlpXHlyhV8fX3zneN2LZPJRPPmzWnevDm1a9dm6dKl9OjRo0Bd1apVSUxMdLzetUJDQzl48CA2m80xVfJaubm5DB06lG7duhEREVFs7337ul9p3L4Jb2965/fbAbzlWDdu4UTefnEuqckXGTD1OVLOJPPqN3lX/Ptp9Va+fOsL/Mv789qKN/Dy9cZus9H1yQcZ2nEQ5ivmwnZ5fWxWLr81m4AZM8FoJGvVSqwJCXh1exAA84rllGrTBq/ISOwWC/bsHC7FTHZs7j/5FYx+ftitFi6/ORv7lStFy3ONVavW0blzB377bQvmzCyeevp5x7plyz7i2WdHkZiYxKKFcyhfviwGA+zefYBBg/POwRv30gjKBvoz561pQF4Ht+XfoooWymrj7MsLqP7R5LzbAXz5HdlHThL4z84AXPxsNRWG9sYtwI+QVwYCYLdYORqdl/3sy+9QZdYLGDzcyDmZxOlRs4uW50+2rdtGsw73sXDLv/NuB/DCG451UxbF8MaLed3fYa8OIelMEm8unQXAllU/8Ombn3Eq/hS/btjOO2vnY7fbWbV4NQmHThS2u+viZjIypkc4A9+NxWazE93sbmoFBfLlj3nTM//+t7zpyev2HqdlnSp4lXJ3bNuwakU63luDPm98hclo4O5K5enZsl6R8oiIiMjtYc+ePVStWpUqVfLOf4+KiuL7778vcH2Kjz/+mMjISPbu3Vss+zXY/xftlb9IWFgYO3fuzLdszpw5eHt7Oy5OsmbNGmJiYlixYgX79+9n4cKFfPDB1amAo0ePpmXLlnTv3t3p6x07dgyj0Ui1atUAmDVrFpcvX2bixIlOM82YMYPU1FQmT56Mh4cHycnJbN26lejoaIYNG0b16tUZNmwYBoOBhIQE4uPjuf/++xk9ejRlypRh3Lhx1/3+e1R98Lpr/5fm1yj6FQH/ClV+PFrSEZzaHlLw6qiu4gVL4RfiKUnLFnQu6QiF8ooqeBsEERERuX4pndqW6P7Lf1vwVKprrV69ms2bNzN1at51DJYuXcqePXvyjQ+SkpJ44YUXWLRoEePGjaNdu3Z07ly07y+3dMfNbDbTps3Vy8D369cPgIULF7J8+XLMZjOhoaEsWrSIwMBA4uLi6NixY77XiIiIYPHixXTv3t3pPjIzM5kyZQrp6emYTCaqVq1KTExMoZmGDx/O7NmziYqKolSpUnh5eTF06FAApk6dyvTp0+nUqRNeXl74+/szatQotm/fzrJly6hduzbR0dEAPP/887RtW7IfWhERERGRO80XX3zBF1984Xj+8MMP8/DDDzueO+t7/flCbVOnTmXkyJH/7/UtbsQt3XG706njdmPUcbtx6rjdOHXcREREiib5/pJtXlT4/r933Hbu3MncuXMds/jeeecdAAYMGOCo6dChg+Nxamoqnp6evPLKKwWaSDfilu64iYiIiIiI/C81bNiQhIQETp06RcWKFYmLi+P111/PV7Nu3TrH4zFjxtCuXbsiDdpAA7ebNmjQIE6fPp1v2ciRI/Pd0FtERERERG4vbm5uTJw4kaeeegqr1UrPnj0JDQ1l8eLFAPTp0+ev2e9f8qp3gHnz5pV0BBERERGR2479Ou+lVpLatm1b4HoUhQ3Ypk+fXiz7vOPu4yYiIiIiInKrUcdNRERERERch93w/9fcgdRxExERERERcXEauImIiIiIiLg4TZUUERERERGXcStcnKQkqOMmIiIiIiLi4tRxExERERERl2G36eIkzqjjJiIiIiIi4uI0cBMREREREXFxmip5Cws0eJR0BKdOHg4s6QhO2ezxJR3BKYvVdX8/cTO4ZjZL3JqSjuCUW3gzzItfLukYhfLqM7mkI4iIiPy/dHES51zzW5mIiIiIiIg4aOAmIiIiIiLi4jRVUkREREREXIbdrqtKOqOOm4iIiIiIiItTx01ERERERFyGLk7inDpuIiIiIiIiLk4DNxERERERERenqZIiIiIiIuIy7DZdnMQZddxERERERERcnDpuIiIiIiLiMuz2kk7gmtRxExERERERcXEauImIiIiIiLg4TZUUERERERGXoYuTOKeOm4iIiIiIiItTx+0O0OflJ2nYPowccw7/HjmXk/uPF6hp/3hnOj0ZRYVqwQwP68eV1Mv51le7pyYvfTONdwbPYvuqn4oll1+7MO6K6Y/BaCRl8Xecm7ck3/rAh9oQ/NxDANgys0gY+w7mAwkYSrlz99dTMZZyw2AycTFuK2df/7xYMv1h1hsxPNC5A5lmM/37j2Dnrn0Faj54fxZtwltwKT3vWPV/agS7d+93rG/a5F5+2LKCPo8MZMmSuCJnKt02jMqTnsZgMnLh829JevvrfOsDurel4sAeAFgzsjg9bj7m3xKuFhiN1Il9ndykCxzrN6XIef5swOQB3Nf+PrLN2bzxwhsc3Xe0QM2oN0cRek8oFouFw7sOM2fsHKwWKw1bNGTi+xM5d+ocAD+u/pHFby4uciZTvSZ4/v1ZMBjJ/XE1OWu/zL8+tCFez76M7Xzefi27fiRn1WcYAsrh+cRIDH4BYLOT+8MqctcvK3Kea/1wJJEZq3dgs9l5qHENngyvl2/9wh9+Y+WeEwBYbXaOn09n/ajueLq78eSH35NrtWGx2ehYrwrPtW9YrNlERERKkjpuzmngdptr2C6MCtWDeandEGqEhfLo1GeY1n1sgbr47YfYs247oz6fXGCdwWik55hH2b9pd/EFMxqpOvUZDveZRE7iBeqtnEHa2m1kHTntKMk5lcTBXuOxXsqgTPvGVPvXQH7rNhp7di6H/jERW2YWBjcTd38zjUvrd5Cx43CxRHugcwdCa1Xn7nqtad6sMfPmvsrfWndzWjt67BSngzKj0cir08axdu2GYsmE0UiVKQOIf+RlchMvUGfFTC59u42sI6ccJTmnkjjyj5ewXsrAr11jqkwfxOHoUY715Z/sSlb8KUylvYsn0zWatm9KpWqVeKrNU9QJq8PgqYMZET2iQN36pet5bdhrALw450Uie0ey8pOVAOz/ZT+T+k0qvlAGI54PDyLzrZewp53He/SbWPb8jO3cyXxl1vh9mOf/ab9WK9lfv4ft1FEo5YXPmLew/razwLY3y2qz8erKX1nwWHsq+nnxyHvf0rZOJWpWKOOo6duqLn1b1QVg46EzfLL1EGW8S2G323nvifZ4l3In12qj37+/o3WtYO6pUq5YsomIiIhruqWnSoaFhRVYNmfOHMLDw4mOjiYiIoLBgwcTHx+fr+bAgQPUqVOHzZs351tet25doqOj6dq1K0OHDsVsNgMwf/58oqKi6NatG9HR0ezeXfgAJjc3l5kzZxIREUHXrl3p1asXGzduBCAjI4OJEyfSsWNHoqKieOSRR9i9ezfZ2dn06tWLBx98kKioKN56662iHhqHRhH3sXXJBgCO7TyCd2lvypT3L1B3av9xLpxOcfoa9/d9gB2rfib9wqViy+UTFkp2QiLZJ5Ow51q4uGwLAZHN8tVc+fUQ1ksZeY93HMIjuKxjnS0zCwCDmwmDu6lYrxvbrVskH3/6FQA/b9tBGf8yBAVVuKHXGDzoSZZ8E0dyyoViyeTdKJTshHPk/H68UldspkxE/uOVsf2g43hl7DyE+zXHyz2oLGXub8qFz78tljx/1iKiBd9//T0Ah3YewsfPh4AKAQXqfl3/q+Px4V2HKRf81w02jNVqY0s5i/3CObBasGzfiNu9La5rW3t6at6gDSDbjPXcKQz+Zf/7Rjdg35mLVAksTeVAX9zdTEQ2uIsNh84UWr9q7wk6N6wKgMFgwLuUOwAWqw2L1Y7BoF8mRUREbne39MCtMH379mXZsmWsXbuWLl268MQTT3Dx4kXH+tjYWJo0aUJcXP5OiaenJ8uWLSM2NhZ3d3c+//xzdu7cyYYNG/jmm29YsWIFH374IUFBQYXu+8033yQlJYXY2FhiY2NZsGABGRl5X6bHjx9PmTJlWLt2LXFxcUyfPp3U1FQ8PDxYtGgRy5cvZ+nSpWzevJldu3YVy7Hwr1iWi2evDh5Sz13EP+j6v4D6VwwkLLIZGz5dWyx5/uARFEjO2fOO5zmJF3D/L7nK9+7IpfU7ri4wGqm/9g0a7VlI+qbdZOw8UmzZKoUEcfrUWcfzM6cTqRTi/G/+Ssxodmz/ltdfm4SHhwcAISFBdI/uzDvvflxsmTyCyhY8XhULP15lH+5E+jXHq9KkpzgzbRHY/pobo5QLKkdK4tWB//lz5ykXVPigzORmokOPDmzfuN2x7O7GdzN39VxiFsVwV+27ipzJ6F8OW+rVTLbU8xjKFDxmpup18X5pHl6DYjAGF9yvIbACpio1sSYcKnKmPySnmwnyu9r5rOjnRXK62WmtOcfCj/Hn6Fi3smOZ1WbjH/NX0+G1pbSoWZGGlYtvUCkiIlLS7PaS/c9V3ZYDt2t16dKFVq1asWLFCgDsdjtr1qxh+vTpbNmyhezsbKfbNW3alBMnTpCSkkJAQIDjS3lgYCAVK1Z0uo3ZbObLL79kwoQJjvpy5crRpUsXTp48ye7duxk+fDhGY95hr1KlCu3atcNgMODj4wOAxWLBYrEU2y/oTl/mBj6RvSf24+vpn2C32Yolj4OzYIXkKv23BpTr05FT064ZCNls7I94nt1Nn8InLBSvOkX/on81WsFsdifZxo1/lfoN2tCiZRQBgf68OOo5AN54fTJjX5qGrTiP2Q38HX1bNqTswx05++oiAPzub4rlfBrmvQXPOfsrOTtmfxg0dRD7tu1j/7a8cwLj98XTt2VfBncezPKFy5nw3oT/SUbrqaNcmfAEmdMGkbNhBV4DJuYvKOWJ1zPjyf7qHcjKLLb92il4bAr7n/ymw2dpdFc5yniXciwzGY38Z2Bn1jz/IPvOXCQ+Ka3YsomIiIhruiPOcatXrx7Hjh0DYPv27VSuXJm77rqL5s2bs3HjRiIiIvLVWywWNm3aRHh4OK1atWLevHlERkbSsmVLunTpQrNmzZzthhMnThAcHIyvr2+BdUeOHKFu3bqYTCan21qtVnr06MHJkyf55z//yb333nvT77f9Y50J73M/AAm7jxIYcvXX+ICgQNKSLha2aQFV76nBM3PyzlXyDShNw3aNsVqt7Fr7y03ng7yOkUfI1Y6MR3BZcp3k8qpblWqvDeLwY69g/dMFUwCs6Zlc/nEfZdqFYT508+cfDXz2Cfr3fwSAX3/dReUqIY51lSoHczYxqcA2584l572XnBwWLfqC50c8C0CTxvfw6SdvA1CuXCAPdO6AxWJh+fI1N53P6fFKLni8PO+uyl0zBnH08RisaXnHy6dpXcp0aoZf+yYYS3lgKu1N1dkjODF81k3nAej6eFci+0QCcGTPEcoHl3esKxdUjgtJzqeJ/nP4PykTWIY5Y+Y4lpmvXO02/br+VwZNGYRfgB/pqek3nc+Wdh73gKuZjAHlsF/6U6ZrBmPW/b9A70EYfPywZ6SD0YTX0+PJ3bYey64fbzqHMxX9vDmXfnXfSelmypf2clq7et8JOjdw/sOEn5cHTatV4If4c9SqWHAKtIiIyK1IFydx7rbvuP1ZXFwcUVFRQF43LjY21rEuKyuL6OhoevbsSUhICL169cLHx4clS5YQExNDYGAgI0aMYMmSJYW9/E0zmUwsW7aMjRs3smfPHg4fvvkLbaz/eDUxXUYR02UUO9duo2WPdgDUCAvFfDmTSynX/+v82PBBjGn9HGNaP8f2VT/x6YT3ijxoA8jYdYRS1YPxqFIBg7sbgdGtSf3T63qElKPWe6M5Pmw22ceuTl10C/TD9Ps0M4OnB37h92I+Wvj5Qddj/oJFNL0vgqb3RbB8+Roee6QXAM2bNSb9UrpjkHata897e/DBzuw/cBCA0DotqVW7BbVqt+DrJXEMHvpSkQZtAJm78x+vgG7hXPp2W74a95By1Hh3LCeGzyb7+NXjlfivj9nfvD8HWj1DwuCZXP5xT5EHbQCxH8Uy5IEhDHlgCFvXbOX+nnk/FtQJq0PG5QxSk1MLbBPZO5LGbRrzr8H/yteRCyh/9Xy42vfWxmA0FGnQBmA7cRhjhRAMZSuCyQ23Jm2x7Ml/RVSD39X9GqvWBoMhb9AGeD42HNu5U+Su+6ZIOZypHxLIyQuXOZN6hVyLlTX7TtK2TqUCdZdmv/cDAAAgAElEQVSzctiekEL7u69Ok7yYkUW6OQeArFwLPx87R/VypYs9o4iIiLiWO6LjduDAARo0aIDVamXt2rWsW7eOBQsWYLfbSUtL48qVK/j6+jrOcfszk8lE8+bNad68ObVr12bp0qX06NGjQF3VqlVJTEx0vN61QkNDOXjwIDabzTFV0hk/Pz+aN2/O5s2bqV27dpHf+971O2jYvjHTNs4lx5zNh6Pedqwb9uFLLBw9n0vJqdzftwuRA6IpU96fSatfZ+/6HSwas6DI+y+U1cbJ8e9R57OXwWjk/Bffk3X4FOUfy+vgpHy8hpAR/8AtoDRVpw0AwG6xcqDLKNwrBlB99lAMRiMYjaSu+IFL3/363/Z2Q1au+p7OnTtw6LcfyDSbeeqp5x3rViz7iGeeHUViYhIfL5pLufKBGAwGdu/ez3ODxhRbhgKsNk5PeJeaH0/Kux3A78er7KOdAbjwyWqChvXGFFCaylMGOLY51PWFvy7TNX5Z9wv3tb+PDzZ/QLY5m1kjrw4MJy+czJuj3+Ri0kUGTxtM8plkXl/6OnD1sv+turQi6rEorBYrOVk5/Gvwv4oeymYj64v5eA+eAkYTuVvXYks8iXt4FwByN6/ELaw17uFRYLNCbg7mf08HwFSzPu7NO2I9cxzvsXMByF6+KK8rVwzcTEbGdGnCwI83YrPbiA6rQa0KZfjyl7wLKf39vloArPvtNC1rBuHlcfWf6vOXs5iw9CdsNjs2O0TUr0IbJ4M+ERERub0Y7P/tRBQXFxYWxs6dO/MtmzNnDt7e3vTv3x+ANWvWEBMTw4oVK9i/fz8LFy7kgw8+cNSPHj2ali1b0r17d6evd+zYMYxGI9WqVQNg1qxZXL58mYkT/3QuzO9mzJhBamoqkydPxsPDg+TkZLZu3Up0dDTDhg2jevXqDBs2DIPBQEJCAvHx8TRu3Bg3Nzf8/PzIysriySef5Omnn6Z9+/b/9f0/Va3XjR6y/4kBudaSjuBUy5Rt/39RCfglqGlJRyjUOHJLOoJTX3R1zX+23MKdT6N2FV59Ct7uQ0RExNUcbRBZovuvua9oM6X+Krd0x81sNtOmTRvH8379+gGwcOFCli9fjtlsJjQ0lEWLFhEYGEhcXBwdO3bM9xoREREsXryY7t27O91HZmYmU6ZMIT09HZPJRNWqVYmJiSk00/Dhw5k9ezZRUVGUKlUKLy8vhg4dCsDUqVOZPn06nTp1wsvLC39/f0aNGkVycjJjxozBarVit9vp3Lnz/ztoExERERGRO8ct3XG706njdmPUcbtx6rjdGHXcREREik4dN+du6Y6biIiIiIjcXuzFfBeq24UGbjdp0KBBnD59Ot+ykSNHEh4eXkKJRERERETkdqWB202aN29eSUcQEREREbnt2Oy6j5szd9x93ERERERERG41GriJiIiIiIi4OE2VFBERERERl2HXVEmn1HETERERERFxceq4iYiIiIiIy7Db1HFzRh03ERERERERF6eBm4iIiIiIiIvTVEkREREREXEZdntJJ3BN6riJiIiIiIi4OHXcbmEGXPPEzbNWr5KO4FSQb0BJR3Aqx2Iq6QiFKuvumtnSd6aVdATndm7G7z7vkk7hlFtUZ8xr5pZ0DKe8IgeXdAQREXEhujiJc+q4iYgUE1cdtImIiMitTwM3ERERERERF6epkiIiIiIi4jJsdk2VdEYdNxERERERERenjpuIiIiIiLgMuzpuTqnjJiIiIiIi4uI0cBMREREREXFxmiopIiIiIiIuw24v6QSuSR03ERERERERF6eOm4iIiIiIuAzdDsA5ddxERERERERcnAZuIiIiIiIiLk5TJUVERERExGXoPm7OqeMmIiIiIiLi4tRxuwP0frkfDds3JseczYcj53Fy//ECNe0f70zHJ6OoUC2IEWFPciX1cr711e6pydhvpvHO4FnsWPVTseSq0P4eGr7yOJiMnPx0PUfmrsi33rdWCGGzB1CmYTV+m/4fjs6PA8BYyp3WSydi9HDD4GbibOzPHHrt62LJ9IeYV8fSoVM4ZnMWIwaNY9+e3wqtfWX6WP7xz4eoc1czACIeaM+ol4Zgs9mwWKxMemk6v/y8s8iZyrQLo+orT2IwGkle/B2Jc7/Jt77sQ20IGdQdAGtmFglj3iXzQAIeIWWp+eZQ3CsEYLfZSP7kW5I+iCtynj97bFJ/7m3fmGxzNu+OnMuJfccK1HR84gE6P9mVitWCGdjoCcfnrMuAaP4W3QYAk5uJkFqVeC6sHxmXrhQpU6kW91Fm+GAMJiMZy1dy5ePF+dZ7hv8Nv2f6YbfZwWrl0ux55OzZB4DB14eAsSNxq1kd7HbSpr5Gzr4DRcpzLVPdxnj2eAaMRnK3riXnu6/yr6/VEK+nx2O7kASAZc+P5Kz+HNzc8R72L3BzB6MRy64fyFn1WbHl+uHACWYs2YTNZuehlvV4slPTfOsXfr+Dlb8eAsBqs3H8XCrrpz1FGR9PPt2wiyVb92O3Q4+W9Xm0faNiyyUiInKn08DtNtegXRgVqgczrt0QaoSF8sjUp3m1+0sF6uK3H2TPuu2M/HxSgXUGo5GeYx5l/6ZdxRfMaOCeV/vx4z9exZx4gbarp3Bu7Q4uHz7jKMlJu8Le8YsI7pz/i6MtO5cfek7BmpmNwc1E+PKXSf5+N6k74oslWoeO4VSveRetm3ahcdN7ePX1CXTr9E+ntfc0qo9fGb98y7Zs+om1q9YDULdebeb/eybtWjxYtFBGI9WmPc3B3pPJSbxA/ZUzSFvzC+Yjpx0l2aeSONBzAtZLGZRpH0b1Gc+yv+sY7BYbJ2IWkbn3GEYfTxqsnkn6pt35ti2qe9s3pmL1YEa2HUTNsNr0m/IMk7qPKVB35NeD7Pr+V176/JV8y1e+s4yV7ywDIOz+pnR+qluRB20Yjfi/MIzzw0ZhTU6hwr/nk7X5RywJJxwl2b/uIHnzjwC41axB4NSJJPfuC4D/iMFk/fQLmeMmg5sbBs9SRctzLYMRz78PJHPeeOxpF/AeOQvLvp+xnTuVr8x6dD/md2Pyb2vJJXPOS5CTBUYT3sNnYPltO7aEQ0WOZbXZePXLDSwY1J2K/r48MvML2jaoQc3gQEdN3/sb0/f+xgBs3HucTzbsooyPJ/FnL7Bk634+eeEfuJtMDJq/jPD61ahawb/IuURE5M6i+7g5d0tPlQwLCyuwbM6cOYSHhxMdHU1ERASDBw8mPj7/F/oDBw5Qp04dNm/enG953bp1iY6OpmvXrgwdOhSz2QzA/PnziYqKolu3bkRHR7N79+5CM+Xm5jJz5kwiIiLo2rUrvXr1YuPGjQBkZGQwceJEOnbsSFRUFI888ki+17JarXTv3p0BAwbc9DH5s0YR9/HTkrz9H9t5BO/SPpQpX/CL1Kn9CVw4neL0NTr07cz2VT9x+UJ6seUKCKtFxvEkMk8mY8+1cmbpVoIim+SryTmfTtquY9gs1gLbWzOzATC6mzC4mYr1f+ERXdrz1efLAdjx6x78/EpToWK5AnVGo5Hxk19g6qTX8y3PzDA7Hnv5eFEcyXzDapGVkEj2ySTsuRYuLttCQGSzfDVXfj2E9VJG3uMdh/EILgtAbnIqmXvzul+2jCyy4k/j/vu64tK4UzO2fL0BgKM7D+Pt50OZCgEF6k7sP875Qj5nf2gR3Zqtyzb/15rr4VHvbiynz2A9mwgWC5nfrcOzzd/y1djNWY7HRi9Px+fI4O2NR6N7yFyxMm+lxYL9SkaRMzn2VbU2tpRE7BeSwGrBsmMTbg1bXP8L5Pye2+QGpuL7/O87kUSV8v5ULlcGdzcTkY1rs2Fvwc7pH1btOEznJqEAHEu6yD1Vg/DycMfNZKRJrUqs23O0WHKJiIjIbdpx69u3L/379wdg5cqVPPHEE6xYsYLAwLxfjWNjY2nSpAlxcXGEh4c7tvP09GTZsrxf/V944QU+//xzGjVqxIYNG/jmm2/w8PDg4sWL5ObmFrrvN998k5SUFGJjY/Hw8OD8+fNs27YNgPHjx1O5cmXWrl2L0Wjk1KlTHD169YvNRx99RM2aNblypYidhmsEVAzk4tkLjuep5y7gHxTIpZS069rev2IgYZHNeb3PZKrfW6vYcnkGB2C+Jpc58SIBjW/g9Y0G2q2dik/1II5/uJbUncX3BTEouCJnz5xzPE88m0RQcEWSk87nq+v39D9Zu3p9geUAnaPuZ8yEYZQrX5bHez9X5EweQWXJueZ45SRewKdxaKH15ft0JG19wemZHpXL492gOhk7Dhc507UCggK5ePbqcbh47gKBFQO5lJx6Q6/j4enBPW3D+GjC+0XOZCxfDmtysuO5Nfk8HvXrFqjzbNsav4FPYQrw58ILed1ot0rB2NIu4T/+RdxDa5J78DCXZs3DnpVVYPubyuZfFlva1QGsLe08pqp1CtSZqt+N9+g52C9dIHvpv7GdO5m3wmDEe9RsjOWDydkch+1E8fw9k9MyCPL3dTyv6O/L3hPnnNaac3L58bcTjO3VFoBawWWZG/sTaRlmSrm7seXACerdVaFYcomIyJ1F93Fz7pbuuF2PLl260KpVK1asyDt/ym63s2bNGqZPn86WLVvIzs52ul3Tpk05ceIEKSkpBAQE4OHhAUBgYCAVK1Z0uo3ZbObLL79kwoQJjvpy5crRpUsXTp48ye7duxk+fDhGY95hr1KlCu3atQPg3LlzbNiwgV69ehXn2wdDwQ/+jfw4//DEviyZ/gl2m60YQ4HBSa4bCmazs6HjS6wJG4x/WE1K3135L81m/1O2ikHliYqO4MN3nZ9btDrue9q1eJD+jw5l1NjBxRDKybJCDpff3xpQoc/9nJr6Ub7lRm9Par//Iicm/hvrFbPzjW823nUcs+sR1vE+jvx6sOjTJPNCFVzmJFPWxi0k9+7LhdETKf1Mv7yFJhPutUPJWLKclCcGYDdn4ft4n6Jn+m/+lM16Op4rLz9J5r+GkLMpFq+nxl9TayNzxlCuTOyLqWptjMFViyeCkw+V0/+tApv2HadR9WDK+HgCUCMokH4dG/PsvGUMmr+c2pXKYTLe9v8XIyIi8j9zW3bc/qxevXocO5Y33Wf79u1UrlyZu+66i+bNm7Nx40YiIiLy1VssFjZt2kR4eDitWrVi3rx5REZG0rJlS7p06UKzZs2c7YYTJ04QHByMr69vgXVHjhyhbt26mEwmp9tOmzaNUaNGkZFR9OlY7R6LpE2fjgAc3x1PYMjVaXEBQWW5lHTxul+r2j01eXrOcAB8A/xo0C4Mm9XKrrW/FCmj+exFvK7J5RUcSNa5G+vOAFjSM7nw429UaH8vlw/e/DlbT/TvzT8fzxs07965j5BKQY51wSEVSTqXnK++fsO6VKt+F1u2502l8/L2ZMuvK2ndtEu+up+3bqdq9SoEBPqTevH6upzO5CRewOOa4+URXJbccwX/jl51q1J95nMcevQVLKlXBz8GNxOh74/i/JJNpK76+aZzXKvj451p17sTAMf2xBMYcnU6aWBQWVJvsNsG0KJba7Yu31Is+WzJKZgqXO34mCqUw3q+YHf0Dzm79uBWKQRjGT+sySlYU1LIPXAQAPP6TZR+rPgGbra0C7j7l3c8N/qXw57+p79n1tXBtfXAr/D3gRh8/LBnXDNl2ZyB9cheTHUbY0s8QVFV9PflXNrVz01S2hXK+/k4rV294widm9TOt+yhlvV5qGV9AN5a8SMV/Qv+WygiIiI35477OTQuLo6oqCggrxsXGxvrWJeVlUV0dDQ9e/YkJCSEXr164ePjw5IlS4iJiSEwMJARI0awZMmSYs20fv16AgMDadCgQbG83oaP1xDTZRQxXUaxa+0vtOiRN5WpRlgo5suZ1z1NEmBs+CDGts77b8eqn/h0wvtFHrQBpO06ik+NILzvKo/B3USl7i05t3b7dW3rUbY0bn7eABg93Skf3oAr8WeLlGfRB58T2bYXkW17sTpuHb16511MpHHTe7icfqXAdMh1326icd12tGwUSctGkZgzsxyDtmrVqzjqGtxTFw939yIN2gCu7IrHs3owpapUwODuRmB0a1L/9HfwqFSO2u+/yNGhb5J1LDHfuuqvD8J85Azn3s1/5c6i+O6j1Yzv8gLju7zA9rXbaN2zHQA1w2qTeTnzhqdJepX25u4W9dixdlux5Mv57SBuVSphCg4CNze8O3Yga/PWfDWmyiGOx+61QzG4u2O7lI7tYirWpGTc7sr7W5Zq2pjchKIPjP5gO3kYY/kQDIEVweSGW+M2WPbmH1AbSl89F9V4V20wGLBnpGPw9QOv3wdT7h6Y6jTCllQ8F5qpf1dFTqakcebCJXItVtbsOEzbhtUL1F02Z7M9/gztG9bIt/zi5UwAEi9eZt3uozzwp4GdiIjI9bDbDSX6n6u6IzpuBw4coEGDBlitVtauXcu6detYsGABdrudtLQ0rly5gq+vb75z3K5lMplo3rw5zZs3p3bt2ixdupQePXoUqKtatSqJiYmO17tWaGgoBw8exGazOaZK/mHHjh2sW7eOTZs2kZ2dzZUrVxg5ciQzZ84s8nvfu34HDduHMXXjHHLMOSwcNc+xbuiHY1k0egGXklPp0PcBOg+Ixq+8Py+vnsne9Tv5aMyCIu+/MHarjT0vLaTl4jEYTEZOLt7A5UNnqPb4/QAkfPQ9pcqXoe2aKbiV9gKbnZpPd2ZdmxfxrOBP2FsDMZiMGIwGziz/iaRvi365/T+s+3YTHTqFs2X7KrLMZp4fPMGx7qMv3mbUsJdJOlf4BTa6dOtEz94PYsm1kJWVxcD+I4seymojYdz71PlsIgaTkZTPv8d8+BQVHsvrFid/vJZKI/6BW0Bpqr36DAB2i5X9D7yIb7O7Kf/3dmQeSKDBt3kXUjn16qdcWrej6Ll+t3vddhq1b8zMTW+TY87mvZFzHetGLhzH+y++TVpyKhF9uxD17EOUKe/PtDWz2L1+Bx+MfhuAppHN2bdpN9lm59OXb5jVRtrrcyg3+19gNJERuwrL8QS8H+oGQOY3K/Bq1wbvByKwWyyQnc3F8Vev4HjpjTkETHoJg7sbljOJpE6dUTy5AGw2sr5agPdzMXm3A/jpW2znTuLe6gEAcn9YhVuj1ri3fgBsNsjNxrwob/8Gv0C8Hh0BBiMYjFh2bca6v+g/pgC4mYyM6dWWgW8vx2azEd2iHrWCy/Lllr0A/L11QwDW7TlGy7vvwquUe77tX/hgJZcysnAzGRn793b4eXsWSy4REREBg/1mTkRxEWFhYezcmf8L+5w5c/D29nZcnGTNmjXExMSwYsUK9u/fz8KFC/nggw8c9aNHj6Zly5Z0797d6esdO3YMo9FItWrVAJg1axaXL19m4sSJTjPNmDGD1NRUJk+ejIeHB8nJyWzdupXo6GiGDRtG9erVGTZsGAaDgYSEBOLj4+nYsaNj+59//pl///vfvPPOO//v+3+62t+v6zj9r3XNcv//i0rAoJw9JR3Bqa89XbcrMdc1/5RMDylaB/Ov4nefd0lHKJRbVOeSjlAor8hiOA9URERuGz+HFGyQ/C81P1u8s+uKyy3dcTObzbRp08bxvF+/vAsLLFy4kOXLl2M2mwkNDWXRokUEBgYSFxeXb5AEEBERweLFi+nevbvTfWRmZjJlyhTS09MxmUxUrVqVmJgYp7UAw4cPZ/bs2URFRVGqVCm8vLwYOnQoAFOnTmX69Ol06tQJLy8v/P39GTVqVFEPg4iIiIiI3OZu6Y7bnU4dtxujjtuNU8ftxqjjdnPUcRMRkWup4+bcLd1xExERERGR24u6Ss5p4HaTBg0axOnT+a/kNnLkyHw39BYRERERESkOGrjdpHnz5v3/RSIiIiIickNsLnxJ/pJ0x93HTUREREREpCg2bdpEZGQknTp14t133y2wfvny5XTr1o1u3brRu3dvDh48WOR9auAmIiIiIiJynaxWKzExMbz//vvExcURGxtLfHx8vprKlSvzySefsGLFCgYOHMiECRMKebXrp6mSIiIiIiLiMuwuPlVyz549VK1alSpVqgAQFRXF999/T61atRw1jRs3djxu1KgR586dK/J+1XETERERERG5TklJSQQFBTmeV6xYkaSkpELrv/rqq3z3nr5Z6riJiIiIiIjLsJXw/r/44gu++OILx/OHH36Yhx9+2PHc2W2wDQbnXcKffvqJr776is8++6zIuTRwExERERER+d2fB2p/FhQUlG/qY1JSEhUqVChQd/DgQcaPH897771HQEBAkXNpqqSIiIiIiMh1atiwIQkJCZw6dYqcnBzi4uLo0KFDvpqzZ88yZMgQZsyYQfXq1Ytlv+q4iYiIiIiIy7Dj2hcncXNzY+LEiTz11FNYrVZ69uxJaGgoixcvBqBPnz7MmzePtLQ0Jk+eDIDJZGLJkiVF22+Rk4uIiIiIiNxB2rZtS9u2bfMt69Onj+Px1KlTmTp1arHuUwM3ERERERFxGbaC1/4QNHC7pXm56CmKNb3TSzqCUz0865d0BKdSza47HaC+u2v+E+Fd+WJJR3Aq6bvcko5QqGNfbCnpCE5dMRiBH0s6RqF6nCv6VcBERESKg2t+8xcREREREREH1/w5XURERERE7kg2F784SUlRx01ERERERMTFaeAmIiIiIiLi4jRVUkREREREXIar38etpKjjJiIiIiIi4uLUcRMREREREZdhK+kALkodNxERERERERengZuIiIiIiIiL01RJERERERFxGbo4iXPquImIiIiIiLg4ddxERERERMRl6OIkzqnjJiIiIiIi4uI0cBMREREREXFxmip5B+j5cl/qtQ8jx5zNpyPnc3r/8QI14Y9H0u7JLpSvFsTYsKfISL0MQIWaITzy2kCq1K9O7MzPWfdebLHl8m3TmOCJz4DRSOp/1nJ+wVf51peJbkf5AT0BsGVkcXbC22QdPI5H9UpUmTPaUedRJYjk2Z9w4cPlxZbNFY9Zufb3UnfKE2AycvrTdRyfk//9+tQKoeGbz+LXsDqHX/2ChPlX99v2lzlYMszYrTbsFitbI8cVS6aOkx6jZvtG5JqziRv5Lkn7EgrUlKlSnug5g/D09yVpXwIrRszHlmslsGYwUTOfoWL9amya+SXb3l0JgKmUO4/8ZzxuHm4Y3EwcWrmNLbOW3FQ+t0bN8O43GIwmsr+PI3vpZ07rTDXrUHra22TMiiH3p40A+M37HLIysdtsYLVyecyAm8pQGO/WTSk39lkwmUj/ahVp7/8n33qfDi0JHPI42O3YLVbOT19A1o79AFSY8jzebZtjvZjGqejizVW2/b3cPeUJDL9/zhL+9DnzrhVCg98/Z0de/YIT13zO3Py8qf/GAHzvrozdDvtHLODSr0eKJVfF9vdwzyuPYzAZSfh0PYfnrsi33rdWCE1mD8C/YTUOTP8PR+bHAeAVEkjTOQMpVd4f7HaOf7yOo++vLpZMIiLy19BUSec0cLvN1WvXiPLVg3il3TCqhYXyj6n9eaP7+AJ1x7cfYv+6HQz5fGK+5ZlpV/h60kIaRjQt3mBGIyGTB3L88fFYzl2gxtJZXP7uZ7LjTzlKck6d41jvMdjSM/Bt24SQaYM51uMFco6f4WjXoY7XqbN1EelrthZbNJc8ZkYD9aY/yS//mErW2Qu0XDON5DXbyTh8xlGSm3aFA+MW8n/s3Xlc1NX+x/HXzLDviygQKu5pKiLmkqKgAmoqlpXaDbXt1v1p3WuYa4u5VXar2zXLTHPL1Baz3FfcKs0NF1xBEZFNQGQbtpn5/TEjMjAgmzF1P8/Ho0fwnfP9njfnHEbOnDPfaTL4YZOX+P3xORRn5tRbpJbBfri28OSLfpF4+7cibO54Vo2YVaFc0LTRHF22nfObDhM271n8RgVx8us9FGTlsevt1bQNCzAqryksZu2Y+RTnF6K0UPHM929yZd8pkk7G1SygUond8/8kd85ktJk3cXx3McXHfkGbeK1COdtnXqIk+miFS+TMmoQu53bN6q1mNo83JnDjhemUpKbTdP1C8qIOUxyXUFok//BJ8vbqx7VV2xZ4fjSThKEvAJD9405ur/mZxu+9Xs+5FLR/7zmOG8ZZzx3zuVlunJVk5XJh5goamxhnD84dR3pUNKde+BiFpQqVrXW95fJ791kOPfUu6uQMgrfPJXnnCXLKjf/Tb6zEa5Dx752uRMuZWWvIOhOPhb0NwTvnkXbgjNG5QgghxJ/Bn2qrpL+/f4VjCxcuJDAwkPDwcEJDQ5k4cSKxsbFGZc6dO0e7du04ePCg0fH27dsTHh7O0KFDefXVV1Gr1QB8/vnnPProowwbNozw8HBOnTpVaaaIiAjCwsIYPnw4o0eP5sqVK5UenzBhAuHh4YSEhBAQEEB4eDjh4eGcOHGCr7/+mpCQENq1a0dmZmZdm6pUp9CH+X3DAQDiT17G1tEeJw+XCuUSY+LJTLxZ4XhuRjYJp+PQlmjqLROArV9bCq8lU3w9FV1xCbc3H8AxpKdRGfWJC2iz8wDIP3kBS89GFa7j8IgfRdeSKU6qmL22zLHNXLq2Jv9qCupraeiKNaRs/JUm5f5ALUrPJjv6Crri+u2ryrQJCeDsD4cASDoZh7WTPfaNK7ZT80c6cGHr7wCc+eEgbUL1E7X8jGxSTl9BayJvcX4hAEoLFUpLC3S6mudTtX4QbcoNtGnJUFJC8S97serWu0I560GPU3z4ANrsrJpXUks2ndpRnJBESWIKFJeQu20fDv17GZXR5ReUfq20taFsIxQcP4vmdv1Nwu9wNjHOGldznKkcbHHt1Z4ba6L0+Ys1lGTn10suN//W5F1NJT9Bnytx4294lZvwF6Zncyv6Crpyv3cFaVlknYkHoCSvgJzLN7D1dK2XXEIIIe4PHYoG/c9c/SVW3MaPH8/zzz8PwNatWxk3bhybNm3Czc0NgM2bNxMQEMCWLVsIDAwsPc/GxoafforlGT4AACAASURBVPoJgMjISNatW0eXLl3Yt28fP/74I1ZWVmRmZlJcXFxl/f/+97/p1KkT69evZ8GCBSxevLjK40eOHOGrr77iiy++MMoSFBTE2LFj669hAOcmrmQlZZR+n5WSgbOnG9k3/7g/Uk2x9HSnOPnupKckOR3bLu0qLe/6VCg5+49VOO48rC+3Nx2o12zm2GbWnm6oy2QqSMrEuWvrap+vQ0e39TNAp+P66j0krt5T50yOnq7klMmUk5KJYxNX8tLutpOtqwOF2fnoNPpNDznJmThW449mhVLB+M1zcfVtwolVu0iOruFqG6B080CbcXeMaTNvomrTwbget0ZY9uhD7juvYdf6wXJX0OHwxgeAjsJdmyjaXX/bhFVN3ClOKTP+U9Kx7ly+frAf8Ajuk55D5e5C8stv1lv9lbHxdKOgluPMrnljijKyeeiTf+D4UDOyT1/l4hsr0Rgm4XXK5eVqNP7VyZm41WD8l2Zs2giXjr5knqj5eBJCCCEa2p9qxa06hgwZQu/evdm0Sf/+B51Ox44dO3jvvfc4dOgQhYWm/4jo1q0b165d4+bNm7i6umJlZQWAm5sbTZo0qVbd3bp1IyEhodrHy+rQoQM+Pj7VqqcmFAoTrxrUZvnij1BJLvuenXB9KpTU91cYHVdYWuA4oDu3tx2q1xhm2WYmX/ypfqYjQ9/mt5DpHH/6PZo9G4prz4qThJpnqhhKV76dTJa596V1Wh3Lh8xkUc9X8erSikZt6+l3o1zlduMnov56CWgr7qbPeWMiOVP/Tu68qViHjcCifef6yQAm28VUf+bt+ZWEoS+QPHEWbq+Oq7/6K81l6mD1xpnCQoVjpxYkrtzF4YHT0eQX4vtKeP3EqoffSZWdNT2WTuL0W6spyVXXSy4hhBDij/SXWHErr0OHDqVbFo8fP46Pjw/NmjWjR48e7N+/n9DQUKPyJSUlHDhwgMDAQHr37s2iRYsICwujV69eDBkyhO7du1er3qioKNq2bVvt4/dLYEQovcYMACDhVBwu3u6lj7l4unM79dYflqUyxSkZWHp5lH5v4dWI4rSKW0StH/TlgXdfJf65t9FkGW8Nc+gXQEFMHJr0uq+EmXubFSZnYlsmk423G4Up1c9UaMhflJ5N2tajOPu35tbhCzXO0XXsQPxGBwOQfPoKjmUyOXq6kZtm3BfqzBysnexQqJToNFocvdzIrUFbFmbnk/DbeVoGdSb9UmKNsmozb6J0vzvGlG4e6DLTjcqoWrXD/l/69ygqnZyx9O9BvkZD8dFD6G7pV3h02VkU/34IVev2lJw/XaMMldGkpGPpWWb8ezZCk5ZRafmC42exbOqF0sUJbVZ2vWQwWU9yJja1HGcFSRkUJmVy+4R+q3rqpiO0eGV4veRSJxmPf1svN9Q1GP8KCxU9l03i+oZfSNpa8b2MQgghzIvWfHcrNqi/3IpbeVu2bOHRRx8F9Ktxmzff3e5UUFBAeHg4I0eOxNvbmyeeeAJ7e3s2bNjA7NmzcXNzY9KkSWzYUPUd7SZPnlz6XrWpU6fe8/j9dnD1ThYMmcqCIVM5vfMo3R/vC4CvfxsKcvIbfJskgPr0Jax9vbH0aYLC0gLnoX3J2X3EqIyltwfNPpvB9cgPKbqaVOEazsP6kVVP2yTNvc1un4zDrqUnts08UFiq8BzxCGk7jlfrXJWdNSp7m9Kv3YM6k3vh+j3OMu3Eqt0sHzKT5UNmcnnncTqO7AOAt38rCnPyjbZJ3pHw2zkeHKJ/8aPTyEAu7zpRZR22bo5YO9kBYGFtiW+fjmTEVuz/e9HEXkTp5YOysSdYWGDZuz9Fx341KpM9YQzZE0aTPWE0RYf3k7/0PxQfPQTWNmBjqy9kbYOlXzc01yveWbS2Cs5exLL5A1g80AQsLXAYHERe1GGjMpbNvEu/tm7fGoWlxX2dtAFk12GcFd28TUFSBnatvABwD+xodFOTurgVHYdDS0/sDLl8RvQieWf1cgF0/fjv5Fy+QewXW+sljxBCCNEQ/pIrbufOnaNjx45oNBp27tzJ3r17Wbx4MTqdjqysLHJzc3FwcDB6j1tZKpWKHj160KNHD9q2bcvGjRt5/PHHK63vznvZqnv8j3Qu6iQPBfvz1v5PKFIXseb1z0sfe2n5NNZO/YLstFv0HT+IgS8Nx9HDhWnbF3AuKpq1077A0cOZ139+FxsHW7Q6HUHPDWF+SCQFdd1qpNGSNGsxvitno1AqufXdLgovJ+D69GAAbn2zDY9XRmPh6oT37P8znKMhLnwSAAobaxz6dCHpjU/rlsMEc2wznUbLuenL6bZuhv427WujyL2YSNOxAwG4vmo3Vh7OPLJzPhaOtui0Onz/PpiDgZOxcnfEf3kkAAqVkuQffyE9qvIb7lRX3N5oWgb78dKBDylWF7F18pLSx55cMZltU5aSm5ZF1LvrCP90In0nP0lqTDyn1+8DwN7DmXGb5mDtYItOq6Xbc4NYOnAqDo1dGPrRSyiUShRKBRc2HyFub3TNA2o15C/7BIeZH4BSSVHUNrSJ8ViF6FeBinZV/vERSmdX7F+fA4BCpaLo0B5Kon+veYbKaLTcnLcI7y/no1Aqyf5xJ0Wx13AapX+RKXv9FuxD+uAYPhBKStAVFJISOb/09CYfTMO2e2dULs747v2ajE9Xk7NhR51j6TRaLkxfTlfDOLuxNoq8i4n4GMZZomGc9Swzzpr/fTC/BE5Gk6vmwozldPpsIkorC9TX0jj7z8V1znQnV/SMFfReOw2FSsm1tfvIuXiDFmP1q+RXV+3B2sOZ/jvmluZq/eIgdvWdgnOHZjR/MpDb5xLov1vfhjHvfkvqnlqMKSGEEH8IrRnfIKQhKXQV3pRivvz9/Tl58qTRsYULF2JnZ1d6c5IdO3Ywe/ZsNm3aRExMDCtWrGDZsmWl5adOnUqvXr0YMWKEyetduXIFpVKJr68vAB9//DE5OTm89ZbxLd/viIiIYMqUKRUmaJUdB9M3J7mjf//+fP/996U3VqnKq76j7lmmIfxdmdfQEUxaorVv6AgmDVGb75NTtI15vrbzUveabZv8o2TE1NPt7++DKxnmeSfFXIV5b/x4PMX0Z/8JIYS4f37yfLpB6w830+d+8/yrrBJqtZq+ffuWfv/ss88CsGLFCn7++WfUajVt2rRh5cqVuLm5sWXLFgYOHGh0jdDQUNauXcuIESNM1pGfn8/cuXPJzs5GpVLRvHlzZs+eff9+KINVq1axdOlS0tPTGT58OP369WPevHn3vV4hhBBCCCGE+ftTrbgJY7LiVjOy4lZzsuJWM7LiVnOy4iaEEKK8jQ284jbCTJ/7zftfTCGEEEIIIYQQf66tkg1pwoQJJCYav8o/efJkow/0FkIIIYQQQoj7QSZu1bRo0aKGjiCEEEIIIcRfnrahA5gp2SophBBCCCGEEGZOVtyEEEIIIYQQZkOrMN8btzUkWXETQgghhBBCCDMnEzchhBBCCCGEMHOyVVIIIYQQQghhNuRDpk2TFTchhBBCCCGEMHOy4iaEEEIIIYQwG/JxAKbJipsQQgghhBBCmDmZuAkhhBBCCCGEmZOtkn9iKszzMy4u5Ts1dASTcmwKGzqCSRqsGjpCpezNdK/C9l8eaOgIJrlrNA0doVIt3W81dASTHBqZ5+8lwHdXfVjY9JmGjmHSK9e/bugIQghx32jN80/cBicrbkIIIYQQQghh5mTFTQghhBBCCGE2tGa6q6yhyYqbEEIIIYQQQpg5mbgJIYQQQgghhJmTrZJCCCGEEEIIs6Fr6ABmSlbchBBCCCGEEMLMyYqbEEIIIYQQwmzIxwGYJituQgghhBBCCGHmZOImhBBCCCGEEGZOtkoKIYQQQgghzIa2oQOYKVlxE0IIIYQQQggzJytuQgghhBBCCLMhHwdgmqy4CSGEEEIIIYSZk4mbEEIIIYQQQpg52Sr5P+ixt8fRPtifYnUhayd/TmJMfIUyfcaG0fe5wXj4evKG/4vk3cqp9xxNgjvTec5YFCol8WuiuPTpJqPHHVp7E/Cfl3Dp5Mu5977l8udbALD1dqPbwn9g7eECOh1XV+8lbun2es329NvP0Tm4K0XqIpZNXsi1mKsVygwYO5iQ5x6lia8Xr/iPJ7dMG7Xr+RBPv/UsKgsLcm5l8/6ot+qcySPYjw5z9e11fU0UcQt/NnrcvrU3fp+8hFOnFlx6dz1XDO1VSqmgz875FKRkcuyZD+qcp6xmQZ3pOysChUrJubX7OP7Zpgpl+r4TQfP+XShRF7L7tSXcPBuPg5cbIf95GTsPZ3RaHTHfRHHqqx11yuIV1JmucyJQKJXErd3H+U8rZuk6Zyze/f3QqIs4POkLbp2JB6Dt82G0+lswCoWCuDVRXDSMqy5vjuGBkK5oi0rIuZbKkUlLKM7Or1NO92A/Hpw7DoVKSeKavcSX60+71t50/ORlnDq14PK767n2+ebSxyyc7Hjoo5dweNAHnQ5iJi3m9rHLdcpjil2fbjSa/jKoVGR/v42spd8aPW7fvxdur4wFnQ5diYb09xZTcCKm3nMAWHXvjtOrE0GpQr1lC3lrvjF63LpPbxyefw60OtBoyF74KcVnzuh/jiefwHboo6CDkitXuP3e+1BUVOss92O8d5/0OA89HYQ6Q/888tv733It6lStMwohxJ+ZfI6baTJx+x/TPqgLHi28mB/0L5r7t+aJeS/wnxFvVCh39fhFYvaeYOK6uk84TFIq8Hv3WQ499S7q5AyCt88leecJci7dKC1SnJXL6TdW4jWom9GpuhItZ2atIetMPBb2NgTvnEfagTNG59ZF56CuNGnhxbSgibT0b0PEvL8zd8T0CuUuH79A9N5jTFs32+i4rZMdEXNe5KNxc8lMSsfR3anuoZQKHnrvWY48NZ+CpAz67JhH6o7j5JZrr5iZK/Ec3M3kJVq8OJjcyzewcLSte54yFEoFQXPHsfHp98hNzmTU5tlc2XWcW5eTSss0D/bDpYUnqwMjaeLfiqD54/lu+Cy0Gi2H5nzDzbPxWNrbMGrrHBIOnjE6t6ZZAuaPJ2r0u6iTMwndOocbO06QffluO3n198OxhSebe0fi3rU13d59ll1D38a5nQ+t/hbMzkffQltUQtA3U7mx5yS5V1NJOXCWU/PXo9No8Zs5mg6vDOfUvHW1bzSlgvbvPcfxp+ZRkJRBzx3zubnjOHll+rMkK5cLM1fQePDDFU5/cO440qOiOfXCxygsVahsrWufpdKMSjzemMCNF6ZTkppO0/ULyYs6THFcQmmR/MMnydv7GwBWbVvg+dFMEoa+cF+yOE36J7dem4zm5k3clyym4NAvaK5dKy1SdPwEGYd+AcCiZUtc3plFesRYlI0aYffESNIjxkFREc6z3sa2f3/U22v3Ys/9HO/RS7dz8outdWgoIYQQf2V/qq2S/v7+FY4tXLiQwMBAwsPDCQ0NZeLEicTGxhqVOXfuHO3atePgwYNGx9u3b094eDhDhw7l1VdfRa1WA/D555/z6KOPMmzYMMLDwzl1qvJXPSMiIggLC2P48OGMHj2aK1euVHp8woQJhIeHExISQkBAAOHh4YSHh3PixInS682ZM8fkz1lfOoZ24+iGAwBcOxmLraMdTh4uFcrdiInnVuLN+5bDzb81eVdTyU9IQ1esIXHjb3iFBRiVKUzP5lb0FXQlGqPjBWlZZBlWSEryCsi5fANbT9d6y+Yf+jC/btgPwJWTl7FztMfZRBslxFwlw0Qb9RweyIntR8hMSgcgJyO7zplcurYm/2oK6mv69kra+BtNyk1oi9KzuR19BW2xpsL5Nl5uNA7x5/qaqDpnKa9Jl1ZkxaeSnXATbbGGSz8fpmWocV+2DA3g/A+HAEg9GYe1kz12jV3IT8vi5tl4AIrzCrgVm4SDp1uts7j5tyI3PpU8Q5aEnw7jU25c+YQFEP+9/rkg40QsVs522DR2wamNNxknYtGoi9BptKT9dp6mhklTyv4z6DT6mxNnHI/Fzqv2GQGcy/VnysZfaWyiP7Ojr6Ar158qB1tce7XnhqEvdcUaSuq4+meKTad2FCckUZKYAsUl5G7bh0P/XkZldPkFpV8rbW1Ad3/eTm7Z/kE0N26gSU6GkhIK9uzFpk9v4yyG528Aha0NZd/arlCpUFhbg0qFwsYGTUZ6rbOY03gXQgjxv+UvseI2fvx4nn/+eQC2bt3KuHHj2LRpE25u+n8QN2/eTEBAAFu2bCEwMLD0PBsbG3766ScAIiMjWbduHV26dGHfvn38+OOPWFlZkZmZSXFxcZX1//vf/6ZTp06sX7+eBQsWsHjx4iqPHzlyhK+++oovvvjC6DpnzpwhO7vuf+RXxbmJG1lJGaXfZ6Vk4uzpRvbNrPtab3k2Xq6oy+RQJ2fi1rV1ja9j17QRLh19yTwRV2/ZXJq4lU66AG6lZODq6c7taraRZ0tvVBYqpq57Bxt7W3Yt31I6EawtG0/j9ipIysClBu3VYc5Yzs/+BgsHmzrlMMXe05XcpMzS73OTM/H0b2WiTIZRGQdPV/LT7rapo08jPB5qTsrJ2velnacb+WXqyU/OxL2rcRZbTzfyypZJysTO05XbFxLpPPUprFwd0BQU4d2/C5mnr1Soo+WYfiT8dLjWGQFsPN0oMOrPTJyr2Z92zRtTlJHNQ5/8A8eHmpF9+ioX31iJJr+wTpnKUzVxpzjl7gsTJSnpWHd+sEI5+wGP4D7pOVTuLiS//Ga9ZrhD2cgDTdrdLJqbN7Hs0KFCOevAPjj+/e8oXV24NXUaANr0dPLWrcfju2+hqJDCo0cpOnqs1lnu53jvPC6EB0f2Ie30VQ7NWUPh7fqfkAshxJ+BfI6baX+qFbfqGDJkCL1792bTJv17DnQ6HTt27OC9997j0KFDFBaa/uOmW7duXLt2jZs3b+Lq6oqVlRUAbm5uNGnSpFp1d+vWjYSEhGofL0uj0bBgwQJef/31atVVWwoTe4Z19+lV8qpzmAxSo2uo7KzpsXQSp99aTUmu+t4nVJOpbDVpI5VKhW+nVnz87Hw+HDuH4a88SZMWXnUNVetTG4f461dvTld8n159MN1e1Slzt5ClnTVDvvgnB2d9TXFd+tJUM1UIY6qIjuzYJM5/tongddMIWjOVW+cS0JYY/9PR4dVwtCUa4jf8UvuMleWs5s2PFRYqHDu1IHHlLg4PnI4mvxDfV8LrlsdkRSYbs8KRvD2/kjD0BZInzsLt1XH1nwOq169A4cFDpEeM5dbMN3AwvJincHDAuk9vbo4aTdpjI1HY2GITElL7KPdpvJ9ZvZtVfV5jbdhM8tKy6PPm32qdUQghxF/TX2LFrbwOHTqUblk8fvw4Pj4+NGvWjB49erB//35CQ0ONypeUlHDgwAECAwPp3bs3ixYtIiwsjF69ejFkyBC6d+9erXqjoqJo27ZttY+X9fXXXzNgwAAaN25czZ+y+npHhNJrTH8AEk7F4eLtXvqYi6cb2am36r3Oe1EnZWJbJoetlxvqlOrnUFio6LlsEtc3/ELS1qN1ztM/YhD9xgwE4OqpWNy8G5U+5urpTlZqZmWnVpCZkkHOrWyK1IUUqQu5+Ps5mrb3JfVqcq3zFSQbt5eNtzsF1Wwv1+7taBzWleABXVDaWGLpYEuXRROInrCo1nnKyk3OxMH77nYvBy838sqNKX0Z93Jl9KsPSgsVg5f8k4sbfyVue+1XQkC/wmZXph47LzfUKcYrperkTOy93bmzpmrn7YbakOXK2v1cWatfHe087Snyk+/2e4snA3lgoD97R82vU0bQ96eNUX+6UVjN/ixIyqAwKZPbJ/RbwlM3HaHFK8PrnKk8TUo6lp4epd9beDZCk5ZRafmC42exbOqF0sUJbVb97hzQ3ryJqvHdLCoPD7TplW93LD51GtUD3iicnbHy74ImORnd7dv6nAcOYNXxIQp27apVlvs13tXpd9ss5psohq2IrFU+IYT4K5AVN9P+citu5W3ZsoVHH30U0K/Gbd58985sBQUFhIeHM3LkSLy9vXniiSewt7dnw4YNzJ49Gzc3NyZNmsSGDRuqrGPy5Mml71WbOnXqPY+Xl5qayvbt23nmmWfq+NOa9svqnfx7yDT+PWQaZ3ce4+HH+wLQ3L816pz8P3ybJMCt6DgcWnpi18wDhaUKnxG9SN55vNrnd/347+RcvkFsPb2Rf+/q7bw9ZDJvD5nMiZ2/88jj/QBo6d8GdU5+tbdJApzc+TttH26PUqXEysaKll3akBybWKd8t0/GYd/SE1tDe3mP6EXqjuq118V569jrP5Goh1/l5Ev/Jf2XmHqbtAGknrqCi68nTk09UFqqaDu8J1d3nTAqc3XXCdqP7ANAE/9WFOXkl24bG/DBC9y6nET0l9vqnCUz+gqOLTyxN2RpFt6TxHLj6sbOE/g+od8y7d61NcXZagoMWawNN5Kxe8CdpkMe5trGXwH9nSrbTxjGgfEfolHX/m6Ed2SfjMOuTH96jniEtGr2Z9HN2xQkZWDXSr+K6x7Y0eimJvWl4OxFLJs/gMUDTcDSAofBQeRFGW8RtWzmXfq1dfvWKCwt6n3SBlB84SIqHx9UXp5gYYHNgP4U/vKrURnVAw+Ufm3Rtg0KCwt0t2+jTU3Tb6u01t/AxTqgKyVlbmpSU/drvNs1vvs+2laDupFxsW7PGUIIIf56/pIrbufOnaNjx45oNBp27tzJ3r17Wbx4MTqdjqysLHJzc3FwcDB6j1tZKpWKHj160KNHD9q2bcvGjRt5/PHHK63vznvZqnu8vPPnz5OQkFC6EqhWqwkJCWFXLV8Rrsq5qJO0D+7CzP2fUKQuZN3ri0sfe3H5VNZPXUJ22i0Cxw+i/0vDcPRw4fXt73M+Kpr105bUWw6dRkv0jBX0XjsNhUrJtbX7yLl4gxZjBwBwddUerD2c6b9jLhaOtui0Olq/OIhdfafg3KEZzZ8M5Pa5BPrv1q9+xLz7Lal7ousl2+moE3QO7sr7+xdRpC5k2et3JzmTls9k+dTPyEq7xcDxQxj80gicPVyYvf0jzkSdYPm0z0mOu8GZ/dHM3v4ROq2OA+t3c+PS9Tpl0mm0nJ2+gu7rputvH792H7kXE2k2Vr9KmLBqN9YezvTeOU9/10itDt+/D+ZA4Ov1uo20smz731zJ8K+noFQpObd+P5mXbtDxGf0q79mv9xK/N5rm/f0Ye+hDitVF7InUjyWvh9vy4BOBpJ9PYPT2eUDdboOu02g5NnMFQd9MRaFScmXdfrIv3aB1hH5cxa7eQ9KeaLwGdGHorx+hURdxZNLd95r2WfpPrF0d0RaXcGzGCooN7zEKmDcOpbUlwev1dxdNPx7LsWlf1a7BDDkvTF9O13UzUKiU3FgbRd7FRHwM/Zm4ajdWHs703Dm/dPw3//tgfgmcjCZXzYUZy+n02USUVhaor6Vx9p+L71FjLWi03Jy3CO8v56NQKsn+cSdFsddwGqV/ISx7/RbsQ/rgGD4QSkrQFRSSEln31UjTWTRk/+cTXP/9ASiVqLduoyQ+Htvh+pVG9c8/Y9OvLzZhoVCiQVdYSNYs/d1ei8+fp3Dffhot/RKdRkPJ5cvkb9pcVW1Vul/jvfeM0TR6qDnodGQnphNVh/ElhBDir0mha4g3ONWSv78/J0+eNDq2cOFC7OzsSm9OsmPHDmbPns2mTZuIiYlhxYoVLFu2rLT81KlT6dWrFyNGjDB5vStXrqBUKvH19QXg448/Jicnh7feMn1b/IiICKZMmVJhglbZcaj85iRV/ZymTPIdfc8yDSGwwDwXcjfZ1O/NG+rLE2qrho5QqStWqoaOYFIjjXk+bblrKt7R01y0dP/jt0RXh0Mj8/y9BPjuqk9DR6jUK9e/bugIQghx3yxuen92oVXXy2b6HPunWnFTq9X07du39Ptnn30WgBUrVvDzzz+jVqtp06YNK1euxM3NjS1btjBw4ECja4SGhrJ27VpGjBhhso78/Hzmzp1LdnY2KpWK5s2bM3v2bJNlhRBCCCGEEOKP8KdacRPGZMWtZmTFreZkxa1mZMWt5mTFrXZkxU0I8Vf2WQOvuP2fmT7Hmudf2EIIIYQQQgghSv2ptko2pAkTJpCYaHyXr8mTJxt9oLcQQgghhBBC3A8ycaumRYvq7/bpQgghhBBCCNPkc9xMk62SQgghhBBCCGHmZMVNCCGEEEIIYTbM8xZkDU9W3IQQQgghhBDCzMnETQghhBBCCCHMnGyVFEIIIYQQQpgNraKhE5gnWXETQgghhBBCCDMnK25CCCGEEEIIsyEfB2CarLgJIYQQQgghRA0cOHCAsLAwQkJCWLJkSYXHdTodc+fOJSQkhGHDhhETE1PnOmXiJoQQQgghhBDVpNFomD17NkuXLmXLli1s3ryZ2NhYozIHDhwgPj6enTt3MmfOHGbNmlXnemXiJoQQQgghhDAb2gb+715Onz5N8+bNadq0KVZWVjz66KPs2bPHqMyePXsYMWIECoWCLl26kJ2dTVpaWi1bRE/e4/Yn5oiqoSOY5EFhQ0cwqbnOpqEjmHTNynxvneRbrGnoCCadtzbPsX/DwnyfUs/kejR0BJM0eQ2doHIqM/3VtAA+afZMQ8cw6Z8JXzd0BCGEuO9SU1Px9PQs/b5JkyacPn26yjKenp6kpqbSuHHjWtdrvn9lCCGEEEIIIf7n6Bq4/vXr17N+/frS70eNGsWoUaNKv9fpKiZUKIxf7atOmZqSiZsQQgghhBBCGJSfqJXn6elJSkpK6femVtLKl0lJSanTahvIe9yEEEIIIYQQoto6depEfHw8169fp6ioiC1bttC/f3+jMv3792fjxo3odDqio6NxdHSs88RNVtyEEEIIIYQQZkNrpu8xvsPCwoK33nqLF154AY1Gw8iRI2nTpg1r164FYMyYMfTr14/9+/cTEhKCra0t8+fPr3u9db6CEEIIIYQQQvwP6dev4ywWLAAAIABJREFUH/369TM6NmbMmNKvFQoFb7/9dr3WKVslhRBCCCGEEMLMyYqbEEIIIYQQwmxU57PU/hfJipsQQgghhBBCmDlZcRNCCCGEEEKYjYb+HDdzJStuQgghhBBCCGHmZOImhBBCCCGEEGZOtkoKIYQQQgghzIZWNkuaJCtuQgghhBBCCGHmZMXtL2rI22NpE+xHsbqIHyd/QXJMfIUyLj4ePPXpRGydHUiKiWfDpM/QFGvw7dmep5e8xq3EmwCc336Uff/9EQtrS55b/yYW1hYoVSpitv1O1Mc/1Ete1+AutJzzLAqVkpQ1e0j8dKPR4x6PB9J04ggANHkFxE5dQt65a/VS9x2DZ91ts42TvyD5bHyFMi5NPXhi4URsXRxIPhvPj4Y2u8O7c0te2PgO309cyLmtv+Pk5cZjH/8DBw9ndFodx7/Zy5HlO6qdqWlQZ/rMikCpUnJu7T5OfrapQpk+70TQvH8XStSF7HltCeln41FZWzLi+zdQWen7Km7r7xz9aAMAoZ9NxKWlFwBWTnYUZefz7aCZNWwtYx7BfnScMxaFSknCmihiP/3Z6HGH1t74/eclnDu14MJ767ny+RYAlNaWPLLxLZRWligtVCRtPsKlD76vVYYBsyJoGdyFYnUh2yYvIdVE/zk39WDYwgnYujiQejaezZM+R2vov8rO7/b8IDqPDkKn05F+IZGtry9BU1hMuyHd6T3pcdxbe7N6+NuknLlqMlf/dyJoEazvn22RS0irJNfQTydgY8i19V93c1V2ftgHL9JqQBfyM7JZETK99FqPTHqcTmOCUGfkAHBwwbdcjTpVoU7ffp0JnhWBQqXk7Lp9/G5ibAWXqXt7mbrvdW63vw+h3xtP85nfy6hv5WLj4sCwxa/i6deSmO8OsPetVSbb6o771ZcBz4bReUwQCoWCU2ujOP6V/nexsr68H21k42zP0M8m4uTjQXbiTTb930IKb+fj5NOI8XsXcCsuGYDkk7HsnrEcS3sbRn//Zml9jl5uXPjxFw6887XJtmverzP9DPXGrNvHMROZ+70Tga8h887IJdw0ZB74wYu0MIypNWXGVM/IJ2gV2hWdVkd+Rja7Ir8gLzWrsu4TQog6k48DME1W3P5g/v7+VT6enZ3NmjVr6lRHmyA/3Ft48klQJD/PWMawec+aLBc6bTS/LtvGJ8GRFNzOo+uooNLHrh29yOdDZvD5kBns+++PAJQUFrPi6Xl8NngGnw2ZQZt+nfHxb12nrAAolbR69wVinp7H8b6T8HisD3ZtfYyKFCSkcfqxtzjRP5KEj7+n9b9frnu9ZbQJ9sOthSf/7RfJpunLeHSu6TYLmTaaw8u2sTBI32b+ZdpMoVQQMn00cQdOlx7TarTsnLuGRQOmsHTE23QfG4JHmweqlUmhVNB37ji2jF3A2v5TaBPeE9c23kZlmgX74dzCkzWBkeybuox+88cDoCks5qdR8/k2bCbfDppJs6DONPFvBcDO//uUbwfpj1/ZdpQr247WoKVMUCro9O6zHHn6faL6Tsb7sUdwaGv8MxZl5XL2jZVc+Xyz0XFtYTG/jZzLgQHT2D9gGo2D/XDpWvMx1TLYD9cWnnzZL5Id05cRMne8yXL9po3m2LLtfBk0mYLbeXQ29F9l5zs0caXrs6GsGvomy0Ono1ApaT+sJwA3LyWy8aVPuH7kYqW5WgT74errybK+keyctoyQeaZz9Z0+mmNLt7Osnz5XJ0Ouqs6P+e4A34/9wOT1ji/dzqrBM1k1eKbJSZtCqWDA3HFsGLeAFQOm0G54T9zKja07dX/VN5Jd05Yx0FD3vc519HKjeWBHshPTS4+VFBbz64ffs3/eN5W21R33qy8btfWh85ggVg9/m+WDZtBqgD+uvk0A0315v9qo+4RhJPxyjq/6TSbhl3N0/79hpde7fS2V1YNnsnrwTHbPWA5AcV5B6bHVg2eSfSOd2Ep+ZxVKBUFzx7Fx3AJWD5hCWxOZfYP9cPH1ZGXfSPZMW0b/MmPq3HcH2GhiTJ34YgtrwmbwzeCZXN1zkh7/fMxk/UIIIe4vmbiZmezsbNauXVunazwYGkD0hoMAJJ6MxcbRDgcPlwrlWjzyEOe2/g5A9A8HaB/a7Z7XLsovBEBloUJpoQJd3fcgO/q3puBqCgUJaeiKS7i58Rfcwh42KpNz7CIlt/P0Xx+/hLWXW53rLatdSACnfijTZk52ODS+d5s9WKbNeowP49y2o+SlZ5cey03LKl25K8or4GZsEo5NXKuVqXGXVtyOTyU74SbaYg2xPx+mRWiAcZ7QAC7+cAiA1JNxWDnZY2fIXWLoK6WFCqWFhcmuaj20B5d/+q1aeSrj6t+avKsp5CekoSvWkLTxNzzDjMdSUXo2t6OvoC3RVDhfcyenZe3HVOuQAGIM7ZB8Mg4bJ3vsTfRfs0c6cNHQf2d/OEgbQ3tWdb5SpcLCxgqFSomlrRW5qbcAyIxNIvNKctW5Qo2va11JrqaPdOCSIVfM9wdpHRZwz/MTf79IQVZudZvIiGeXVmTFp3LbMLYubjpM63Jjq1VoAOdM1H2vc4PefoYD89ehK9OPJepCbhy9hKag+J7Z7ldfurf2JvlkHCUFReg0Wq4fuUAbwzg11Zde96mNWoUEEPO9/rkm5vuDtK7G8+4dLr5NsHN3Iul30y8WNCn3nHFp02FalsvcMjSA84bMKYbMd54zkioZU0W56tKvLe2sjfpWCCHEH0cmbg0kLy+PcePG8dhjjzFs2DB2794NwIcffkhCQgLh4eG8//77tbq2UxM3bidllH6fnZKJk6fxZMHO1YGC7Dy0Gv1i9O3kTKMJRdOurfm/bfOJWDHFaIVIoVTwj63zmXL8c+IOnSUxOq5WGcuy9nKjMOnuq/NFyRlVTsyaPD2AW3tP1rnespw83cgu32ZNqm6z7OS77erYxJUHw7px7Ovdldbh4tMIr4eac6OabWbv6UpuUmbp97nJmdiX60d9mbu588qUUSgVPLV9Hs9Gf8b1g2dIK1evV4925Kff5nZ8arXyVMbGyxV1mQwFyRnYeFVvcgqAUkHf3e8SevYLbh44Q9bJmo8pR09Xo/7LScmsMEG2dXWgMDsfnaH/cpIzcbjTf5Wcn5t6i6NLtvLyb58w4einFObkE3/wbLVzOXi6kpNsfF0Hz6pz5SZn4mgoU53zTfEfF8K4HfMJ++BFrJ3tTOcqM7ZykjNxKNdeldVd1bmtQrqSm3KLm+cT7pmxMverL29eSsSneztsXBywsLGiZbAfjt7ulea4X21k18iJvDT9NsO8tCzsGjmVlnNu6kHE1rk89e1MHujerkKmB8N7cXHT4Wpnzq0kc26ZzLnVHFO9Xn+S5w5/QrsRj3D4w/rZIi+EEJXRNfB/5kre49ZArK2tWbRoEQ4ODmRmZjJq1CgGDBhAZGQkly9f5qeffqr1tRWKiscqvEJqspD+f8ln4/mo9z8pyi+kTZAfTy95jU+CI/VFtDo+HzIDGyc7xnwxicZtfUi7lFjrrFUENlnUufdDeI7pz6nwN+pWZ4UMpiLcu83uFBn0dgS731uHTms6t5WdNU8t/hfbZ6+msMyr11VGqqK+qsrcKaTT6vh20EysnOwY/OW/cGvnQ+bFu33VJrxXnVfbDCFMZKjB+VodBwZOx8LJjoeXv4bjgz7kXKjhmDLZVtUf85Wdb+1kR+vQrnzRZxKF2fmEf/YKHR7rzbkff6leLNMDqxrZa3B+OdGrd/PbJz+i00GfyU8Q9Mbf2PH6l+WqvHefmapbp9NVeq6FjRU9Jg7n+2dq94JTmXAm671XmXv1ZWZsEkcWb2bUmmn61e9zCehMrACXVmGy7e9dpqo2qkpeWhZLev6LgqxcGnfyZcSXk1gxcJrRateDw3ux9V+fV36RajxnVPJkV3U44LcPvuO3D76j24Rh+I0P4bDhPbNCCCH+ODJxayA6nY6PPvqIo0ePolQqSU1NJT09/d4nVqJ7RAgBY4IBuHHqCs5lXkl28nQjp9wbyfMzc7BxskepUqLVaHH2ciMnTb8FrOzE4vK+UyjnqrBzdSD/1t0tNAXZ+Vw9fJ42/TrXeeJWmJSBtXej0u+tvNwpTLlVoZxd++a0+fAfxDw9j5JbtdsiVtbDY0MIGG1os9NXcCrfZmlVt5mTlxs5hm1z3p1b8MTCifqcbo60CfZDW6Lhws7jKC1UPLX4X5zZ+Avntx+rdr7c5EwcvO+uPDp4uZGfestEmbu57b3cKtw0oCg7nxu/nadZUOfSiZtCpaTloIf5bsib1FVBUia2ZTLYeLlTYKL/7qUkO5+MX8/jEexXrYmb/9iBdDb0X4qh/24YHnP0dCO3XP+pM3OwdrJDoVKi02hx9HIr3faYk5xp8nzfPh25ff0m6kz9jT4ubT/GAwFtqpy4dRk7kM5j7uZy9LrbNo6ebuSmVp3LoWyulMx7nl9efpmtuqfXRvH48sgKZXKSM3EsM7YcvdzITTPuM1N156VmobK0MHmuS/PGODf1YOz2+aXHn9k6lzXD3yb/5u0qM/8RfQlwZv1+zqzfD0Dg60+Rk5JJZXJS6r+NQN8/9o1dyEvLwr6xS2l/aYpK0BTpn9fSzsSTdS0N15aepJ7W3yjFo30zFColaWfiK/2HO7dcvzp4uZFXLnNuSiYOZTI7VGNMlXVx468MXzFZJm5CiPtKbk5immyVbCCbNm0iMzOTDRs28NNPP9GoUSMKCwtrfb3fV+8qvZnIhZ3H6PJ4IAA+/q0pyFGTe7PiP8xXfztHhyHdAegysi/ndx4HwMHDubTMA34tUSgU5N/Kxc7NERsn/bYrC2tLWvV+iJtxVb/HpzpyomOxaemFdbPGKCwt8BjRm8ydxm++t36gER2+mszFiQtR3+N9RdV1dNUuFg+ZwWJDm/mNvNtmhTnqCn8sQsU2u7hL32af9JnEf/r8i//0+Rfntv7OljdXcMHQnuELXiQ99ga/Ld1Wo3xpp67g7OuJY1MPlJYqWg/vydVdJ4zKxO86QbuRfQBo4t+Kopx88tOysHFzxMrQVyobS3wCO3IrNqn0PJ/AjtyKSyKvij9cqysrOg77lp7YNvNAYanCe0QvUgw/+71YuTtiYciptLGkUWBHcsvkrMrJVbtZOWQmK4fM5PLO4zxkaAcv/1YU5uSXbkcrK+G3c7Qz9F/HkYFcNrRn7O4TJs/PTsrA2781FjZWADTv/RAZsTcqXLes6FW7S28MErujermu/3aOtoZcDz0RSNxOfa64XaZzVaXs+8HahHUj/WLFSXDKqSu4tPDEyTC22g3rSVy5sRW36wQdTNRd2bnpFxP5vOsElvaexNLek8hJzuTrIW/cc9IGf0xfAti567clOnq703ZQN87/9GulmZLvQxvdOeehJ/TPNQ89EUic4TnE1s0RhVK/GubczAOXFk24fS2ttK4Hw3tx8eeqV8hTy9XbdlhPrpTLfGXXCdobMnsaMuffY0y5GG7iAtAypGvpnS+FEEL8sWTFrYHk5OTg7u6OpaUlhw8f5sYN/R+D9vb25OXl1enal6KiaRPchX/t/0j/cQCvf1H62DPLX+enqV+Sk5bFrvfW8uTCVxgQ+STJMdc48e0+ADoM7k73Zwai1WgoLijmu1c+BcCxsQuPf/gyCqUShVJBzJYjXKqP95pptMTNWErHtW+gUClJXbuX/IuJeI4NBSBl1U6avfYEFq6OtH7vBQB0Gi3RYVPrXrfB5b36Nnv1gL7Nfpp8t83+tuJ1fp6ib7Pd767liU9fof9kQ5ut31fldZt1a4vfyEBSzyfw8lb9SsSeD9Zz2cSd/srTabQcfHMlw76egkKl5ML6/dy6dIOHnukPQMzXe7m2N5pm/f3426EPKVEXsTdyCaD/473/xy+hVClBqSBu0xGu7YkuvXab4T2JrY9tkoacZ2esoOda/V0Xr6/dR+7FRJqPHQjAtVW7sfZwJnDHPCwcbUGro+WLg9nX93WsG7vi/99/oDDkTPr5MGm7aj6mruyNpmWwHy8e0LfDtslLSh8buWIyO6YsJTcti/3vrmP4pxMJnPwkqTHxnDH0X2XnJ0fHcXHr74zbMhetRkNazDVOfRMF6CdFA98Zi62bIyOXTybt3DV+iFhQIVeLYD9eOPghxeoitpfJ9fiKyeyYupS81CwOvLuOoZ9OpM/rT5JWLldl5z+6cAJNe7XH1tWBl478l18++oGz6/fTd8ZoGndoDjodtxPT2TX9K5N9tvfNlYxcPQWlSsnZ9fvJuHSDzoaxdfrrvVw1tMnzhrp3GOqu7Nx7eeGXj7FytEVlaUHrsG58/8x7ZF6uOEm/X30JEL74n9i6OqAtLmHXWyspzM6vtC+/G7vgvrTR759tYujnr9BxVD+ykzLY/PJ/AfDp8SCPRI5EW6JBp9Gxe8ZyCm7f/beg7dAe/DjO9F1Ey/brvjdXMmK1/jnj3Pr9ZF66QSdD5jNf7yV+bzS+wX6MO6hvn11l2mfQwgn49GqPjasDzx35L0c++oGY9fvpPW0ULq28QKsj+0Y6e6cvv2d/CyGEqH8Kndwe6g/l7+/PyZMnyczM5B//+AfFxcW0b9+eEydO8OWXX+Lj40NkZCQXL14kMDCQqVMrn5y85fu3PzB59YUU1H7l8H7aY23d0BFMaqw18Z4TM9G0uPL3ADWk89aqho5gktKMn03NdZRpzDUYoDLT/jTnV1z/mWD68+WEEKImGvpv3NnxdftorvvFnJ///5JOntSvJri5ubF+/XqTZT788MM/MpIQQgghhBDCzMnETQghhBBCCGE2tGZ9U/6GIzcnEUIIIYQQQggzJxM3IYQQQgghhDBzslVSCCGEEEIIYTZko6RpsuImhBBCCCGEEGZOVtyEEEIIIYQQZkPb0AHMlKy4CSGEEEIIIYSZk4mbEEIIIYQQQpg52SophBBCCCGEMBvyOW6myYqbEEIIIYQQQpg5mbgJIYQQQgghhJmTrZJCCCGEEEIIsyEbJU2TFTchhBBCCCGEMHOy4iaEEEIIIYQwG/I5bqbJxO1PzFWraOgIJp2ytGnoCCa1KW7oBKadtzLfpyctqoaOIOqJuT7Z68x4P4y5RrMx06eMfnaZXGg7pKFjVOrBS1sbOoIQQtSJbJUUQgghhBBCCDNnri/CCiGEEEIIIf4Hyee4mSYrbkIIIYQQQghh5mTFTQghhBBCCGE2ZL3NNFlxE0IIIYQQQggzJxM3IYQQQgghhDBzslVSCCGEEEIIYTbM9FNPGpysuAkhhBBCCCGEmZMVNyGEEEIIIYTZ0MntSUySFTchhBBCCCGEMHMycRNCCCGEEEIIMydbJYUQQgghhBBmQ25OYpqsuAkhhBBCCCGEmZMVNyGEEEIIIYTZ0MrNSUySidtfUPN+nQmaFYFSpeTsun0c/WxThTJB70TQIrgLxepCdkYuIe1sfJXnNmrfjAHzn8XK3obsxJtse/VzinLVNc7WLKgzgbMiUKiUnFu7jxMmsgW+E0Hz/l0oURey57Ul3Dwbj4OXGwP/8zJ2Hs7otDpivoni9Fc7Ss/pPD6ETuND0ZZouLY3ml/nr6tRLq+gznSbE4FCqSR27T7OfVoxV8CcCB4w5Ppt0hJunYkHoN3zYbT+WxAoFMSuieLiUn2uZkO70ynycZzbeLN9yNtknr5ao0x3PPr2WNoGd6FYXcQPkxeTHBNfoYyrjwdPffoKts4OJMdc5ftJn6Ep1tCiZ3v+tiSSW4lpAJzbfpSo//4IQK9nB9FtdDAoFBxbt5ffvtpeo1zm1pcDZkXQ0jCmt01eQurZiu3k3NSDYQsnYOviQOrZeDZP+hxtsabK862d7Bj0/gs0ausD6Nj2+pcknYilcYdmhM57DpW1JTqNhp1vrCAt+kqFOvsbftdK1IVsK/O7Vj7X0E8nYGPItfVfd3OZOl9lbcno795AZWWB0kLFpa2/8+tHG0qv5z8+BP9xoWg1Gq7sjebAPdqweb/O9DP0Zcy6fRwz0Zf93onA15BjZ6S+LwEGfvAiLQZ0IT8jmzUh043O8Rsfgp8hx9W90fxSzb68H23m6OXG4I9fxt7DGZ1Ox+lvojhhGHePTHqcTmOCUGfkAHBwwbdcjTr1h+QCCPvgRVoZ2nBFmTbsN2MMLQf6oy0uIetaGtsnL6EwO7/Ktmsa1JlH3tH35YW1+4heVLEvH5kdQTPD7+W+SUtIL/NzKJQKHt86h7yUW2wf/yEAAa89Tvun77bP7+9/y/W9FdunuuwDA2g88yUUKiVZ3+0gc8l3Ro87DQvC7cUnAdDmq0mdtYjCC/rnT9ex4bg8FQYKBVnfbufWyp9qnUMIIf5sZKvkH8Df3/8Pq0uhVNB/7jg2jlvAygFTaDe8J25tvI3K+Ab74eLryfK+keyetoz+88bf89yQBS9w6L31rA6dTuz2YwS89GitsvWbO45NYxfwTf8ptA3viWu5bM2D/XBp4cnXgZFETV1Gv/n6bFqNll/mfMM3/afyffgsOo8bWHruA73a0yI0gLWh01k7cBonv9ha41wPzx9H1N8WsDloCr7hPXEql8u7vx9OLTz5uXckR6Yso/u7+lzO7Xxo/bcgtj/6NlsHzuCBEH8cWzQBIOtCIgde+IS0wxdr3FZ3tA3qgnsLTz4Oeo2NM5YyfN5zJsuFThvDr8u28Z/g11DfziNgVHDpY/FHL7BoyAwWDZlROmlr3NaHbqODWRz+JosGT+PB/l1x9/Wsdi5z68uWwX64tvDky36R7Ji+jJC5402W6zdtNMeWbefLoMkU3M6j86ige54/4O0Iru4/zbIBU1g+aAYZsUn6a00fwy+fbGDlkJkc+ugHgqaPqVBfi2A/XH09WdY3kp3TlhEyz3SuvtNHc2zpdpb10+fqZMhV2fmawmK+HT2fVYNmsmrQTFr064yXfysAmvZqT+vQAFaGTWfFwGkcu0cbKpQKggy/96sHTKFtFc8ZK/tGsqfMcwbAue8OsHHsBxWu69OrPS1DA1gTNp2vB07jRDX78n61mVajZd/cb1g+YCprwmfRZexA3Mv8nMeXbmfV4JmsGjzT5KTtfuUCiPnuAN+baMP4g2dYETKNlWEzuHU1mR4ThlXZdgqlgt5zx7E1YgHfBk+hdXhPXMr1ZdP+fji38GRdn0gOTF1Gn3eNf46Ozw/ilmGMl3X6y+38EDaTH8Jm1mnShlJJk7f/j8QX3+LKkJdxGtoPq1ZNjYoUJ6aS8MxU4odPIOOzdXjOeRUAqzbNcXkqjPgnJnF1+AQcgrtj2dzbVC1CCPGXJBO3vxjPLq3Iik/ldsJNtMUaLm46TKvQAKMyrUIDOP/DIQBSTsZh7WSPfWOXKs91benFjSMXALh28Cxthjxc42xNurTidnwq2YbrX/75MC3LZWsRGsAFQ7ZUQza7xi7kp2WVvsJfnFdAZmwSDp5uAHSMGMjxzzahLSoBQJ2RXaNc7v6tyIlPJdeQ69pPh2kaZpzLJyyAK9/rc2WciMPK2R6bxi44t/Em/UQcGnUROo2WtN8u0HRwNwCyY5PIiUuuWSOV0z40gOgNBwFIPBmLjaMdDh4uFcq1fOQhYrYeAeDkDwdpH9qtyut6tH6A6ydjKS4oQqvRcvXIedqHVX1OWebWl61DAogx1JV8Mg4bw5gur9kjHbi49XcAzv5wkDaGzJWdb+Vgi0+Pdpxetw8AbbHm7oqHToe1gy0A1o525Kbdqpgr1Pi61pXkavpIBy4ZcsV8f5DWhvFX1fnF+YUAKC1UKC0s0Bl2lXSJGMiRzzahMbRh/j3asHxfXtpUsS9bmnjOsDPkSPr9IgVZuRWu2yliIMfK5Kh2X96nNstLyypd4So/7hoyF0BiJW147eBZdBr9W/STTsTdM2/jLq3Ijk8lx9CXsT8dxrdcX/qGBnDJ8FyWdsK4L+293Gg+oAsXvtlXnSapFZvObSm6lkTx9RQoLiF7ywEcBvYyKqM+eR5ttr491NEXsPB0B8C6VVPUpy6iKygEjZb838/iGPLIfcsqhGg4ugb+z1zJxO0PpNPpeP/99xk6dCjDhg1j61b9K9BHjhwhIiKCV199lUGDBhEZGYlOV7th4+DpSk5SZun3ucmZODRxrVgmOeNumZRMHDxdqzw34+J1WoZ0BaDtoz1w9Kr+Hzx32Ju4vr1nxWy5SRlGZRzKlXH0aYTHQ81JORkHgEtLT7y7t+OJn2fx2HczaezXska5bD1dyS+TKz85E1sv4zrtPF3JL5MrPykTO09Xsi4k0rhHO6xcHVDZWuHd3w87b/ca1f//7N13XNPX/sfxVxL2FFQMThD3wr0XDgQc2NpeO1S0e3i1Vmu11NZWrb3acdtardQ96qhttW7cW6x71gHiZAiI7Jn8/kiEAAnTlvR3P8/78HErOSfft5/z5Qsn5+Sb4jjWcOGRQbak6AScCtXDzsWRjKRUNPpf8JKi4nEyGPO6bRvy9vbZjFo2GbeGtQCIvXoHj45NsK3igKWNFY18WuPsXvrc5jaWjmoXkgyOlRydgGOh897WxYHMpLS8X4STDfKY6l+lbnXS45Px/+I1grbNxO8/r2Bpaw3Ank9X0fuD53nj2Df0Dn6eg/9ZVyRX4e+15OiiNSicKyUqAUd9m+L6K5QKRm2fxVtn5nPr8AWiz+pq6OKppnbHxry4aTrD1wejblV8DUt7zUgxcs0ojounmlodGzN803SGrQ+mRgk5CuT5i2r2mFPtarg1r0eU/rwDaBPUn6CdnzFg7qtYO9tVSq7itBzek5v7zxfbxs7dhZSo/LFMjU7AvtC1zF7tQqrBuZ4apbuWAXSdPoLjs9YY/fnTYnR/ntn1Gb2+eBUrI/UpLcsaVcmJjsv7e050HJY1TF97qjzjS+rBUwBkXr+FXfsWKKs4orCxxqFXeyzdq5U7ixBC/NPIxO1vFBoayp9//smmTZtYunQpc+bMITZW/96jy5f54IMP2LZtG3fv3uXUqVN95jgpAAAgAElEQVTlO4hCUeRLRX8GG2ujLbZv6Hs/0jqoPy9snYGVgw252TlPJFuRlzWMZshvZGlnjf/C8Ryavops/XvslBZKrJ3t2TBkOkdmrcFv/tgyxipfLrRakm7c5/L8LfRdO4U+qyeTePk2mpzcMh2/4tmMRdM1un8xki+6jeN7/6kcXxbKCyETAXgQfp9DP2xmzKqpBC1/n+grt9DkliG3uY1lCccqMbOJ/kqVihotPDi7ag/LAz4kKy2TTm/ptqu1GdGXvTNW80OX8ez9dDV+c14tGsv44JQie8n9tRotK/yDWdhpHGpvL/178HQ1tHG2Z3XgdA7MWsPgkmpYzmuGkUYFe+jHcl3gdA7PWoN/Kcfyr6wZ6M67IQvHs++TVXnv0z27cjeLerzLcr9gUmMT6f3hi397ruJ0GjsETY6GK78dKbad8WMUbmQ8R92+rUmPSyJO/95dQ5dX7GZNt3fZ4BtMWmwiXaYVrU+pmTi+MXadWuH8rC+xc5cAkBV+h/gff6bu0lnUWTyDjD9von2C11shhDB3cnOSv9GpU6cYOHAgKpWKatWq0aFDBy5cuICDgwOtWrVCrda9x6hJkybcu3eP9u1Lv3XtsZSoBBxr5q+GObi7klpoC1dKdAKOBqsrDmpXUmMSUVlamOz7MDyKX0f8B4Aqnmo8+7Quc7ZUY9liCmWLSsDBYMVK1yYR0G0J8w8Zz7WNR4nYcdKgz0Mituv+Hns2Aq1Wi42rIxkJyaXKlRaVgJ1BLjt3V9KjHxppk5/LrqYrafpc4WsOEL7mAADeU/5FmsEr3uXRaWR/2j+ve4/avXMROBtkc1K7klSoZmkJydg42aNUKdHkanByr0pyrC5bpsENZK7tP8vgmWOwc3Ek7WEyp9bv59T6/QD0f284jwxWA0piDmPZZlQ/Wj2nq1P0+Qicalblnv4xR7UrKfoaPJaekIy1kx0KlRJtrgZHd1dS9JmToxKM99dqSY5KIEq/mnVt24m8iVuLYT3YM30lAFe3huH3n1cAaD2qH62ez89l+L3mqHYlJab4XA6GuQp9rxrrn5mUxp3jV/Do3Yq4a3dJjnrIdX0No8/pamjr6kiOie+H0l4zHApdMwrnKPq8D7mhzxFjkCPdSI6/q2ZKCxVDFo7nym9HuW5w3qXF5W/jPL9mH08vnfi35ipO82d64NW3Deufn11i29SoBBwMdkPYq11JLXQtS41KwN7g+9LeXXctqz+wI/V821K3jzcqa0ssHW3p8+2b7B23gHSD+lz5aR/+yyaWmMWU7Og4LNT5q2QW6mpkxxa9Zlo39kA9azx3XvkITWL+OfNoQyiPNoQCUO3doAKrd0KI/z/krpLGyYrb36i47Y9WVlZ5/61Sqcgty+qHgehzEbh4qnGqUx2lpYrGgzsTset0gTYRu07TdFh3ANRtvMhKTiM1NrHYvrZVnXSdFQo6jQvk/Ko9Zc4Wcy4CZw81jvrnbzikMzcLZbu56zRN9Nlq6LOl6X8B7zP3FRKu3+fsj9sL/nt2nqRWt2aAblKptLQo9aQNIP5sBI6eauz1ueoFduZuaMFcd0NPU/8ZXa6qbb3ISkojQ5/LWl8bu1pVqRPQnlsbj5b62MaErdyVdzORy6Enaf10DwBqt2lAZnI6KQ+K/rJ389hlmgd0AqDNsB5cCdX9UupQ3TmvTS1vLxQKBWkPdbWx1+d2rlmVZn4dOP/7sVJnNIexPLNiN8sDglkeEMz10FM01x/LvY0XmfpzurDbxy7TOKAjoJt4XddnvrH7tNH+qQ8ekRSVgGt9dwDqdWtO/HXd9C4l9iF1OjcFoG635jyMjAbg7IrdeTe5uLGzdLnuHLtMI32u5s/0IFx//oXvMp7L1tURayfddjULa0vqdW9BQrjuhhI3Qk9St6uuhi76GhqbLD0Wcy6CKgbf941Kcc3INBhLUyJCT1Kna/5YqorJ8XfUDGDA3FdIuHGfU4sKnneG71VrOKA9cVfv/q25TPHo1YqObw7it5e/Iicjq9i2ALHnInD2zP++bBDYmVuFxvJW6Gka6a9lbm3zvy9PfL6e1R3G8VOXCex++3vuH7nM3nELAPLeAwfg6deeBH19yiPjwjWsPGpiWbsGWFrgNLAnKXuOF2hj4V6dWvM+JOq9L8iOvFfgMZWrc14bR9+uJG05UO4sQgjxT6PQlvfNVKLU2rRpw5kzZwgNDWXdunWEhITw6NEjhg0bxvr164mIiGDJkiUsXLgQgE8//ZQWLVrw9NNPF/u8X9cdYfTrHj7e9P54hO7W3usOcGLe77Qa0QeA86v2AuAzIwiP3q3ISc8idFIIMfpb1RvrC9DmpQF4j+oHwI0dJzn8edH38zxmWcwZVc/Hmx7Tdc9/ed0BTn33O8312S7ps/WcGUQ9fbY9E0OIPX8T9w6NGPbrR8RduY1WozvA8f+s59a+cygtVfT94jWqNa9LblYuR2b+xL2jl4sc26WYuXDNPt60+0SXK3ztAS59+zsNR+pyXV+py9XhsyDce7ciNz2LYxNC8m7v3/+3aVi7OKDJzuHUJz8Rc/gSALX92tNh5iisqzqSlZTGw0u32PfCnCLHvmKlMR0MGPTpaBr18iYrPZNf31vI/Qu6445cOpmN74eQHJuISx03hn/3b2yr2BN16RY/T/ie3KwcOo3ypeOIfmhyc8nJyGLbjFXcOX0dgFfWf4SdiwO5Oblsn7GKiKOXihxbnWv6tZ3KHMs0I7H6zQjCs5fuWNsnhRCtr9OwZZPYOXkRKbGJONepzpB5Y3W3ar8UydZ3FuTdPMNUf7dmdfH7zysoLS14dDuWbfpbstdq34i++o/OyMnMZteHy3hgZJtZ3xlBePZuRXZ6FjsMvteeXjaJne8vIjUmEee61RmkzxV7KZJt4/NzGetfrUkd/L96HaVKiUKp4OqWMI59sxEApaUKv7mv4aav4f5ZP3Hn6GUsTY6k7vu+58f5Y/nHvN9pqR/LC/qx7D0jfyx3TdKNJYDfd29Tu0tTbFwcSItLIuyrX7i07gBKSxX95+rGUpOVy6FZP3HXyFhmG8nzV9SsVodGPP/LRzwwOO8e3/bf/79v4NasHmi1PLobx66pS4xOqv6KXAADv3ubOl2aYquv4ZGvfuHiugO8fPBLVFYWZDzU3ajj/pkb7P5gKY7FXDLq9PGm6/QRKJRKrq47wJnvfqepfiyv6Mey+8wgavduRU5GFvvfDSGu0EeVuHdpivfrAXkfB+DzzRtUba6rT/KdOA5NWWJ04t7LrnS7Dex7tafGB6+DSsmjDaHE/7COKs8FAJC4dhvqWeNx9O1K9n3dWwm0ORpuDRsPQN2f5qCq4oQ2J4fY2T+Sdqz0d7hscq1sdxwWQlSeVz2erdTj/xj5c8mNKoFM3P4GjyduWq2WOXPmcOjQIRQKBW+++SYBAQGEhYU90YlbZStu4laZipu4VaaSJm6VqbiJW2UyNnEzB0ozPfeBYidulcnYxE0Ur7iJW2Uq7cStssjETYh/Dpm4GScTt38wmbiVjUzcyk4mbmUjE7eyk4lb2cnErXxk4ibEP4dM3IyTm5MIIYQQQgghzIZWbk5ilJm+bi2EEEIIIYQQ4jFZcRNCCCGEEEKYDTPdEV7pZMVNCCGEEEIIIcycTNyEEEIIIYQQwszJVkkhhBBCCCGE2ZCbkxgnK25CCCGEEEIIYeZkxU0IIYQQQghhNuTmJMbJipsQQgghhBBCmDmZuAkhhBBCCCGEmZOtkkIIIYQQQgizodHKzUmMkRU3IYQQQgghhDBzsuImhBBCCCGEMBuy3macTNz+wU4oUio7glGvZlpVdgSjltjkVHYEo17LUFV2BJOuW1hXdgSjzPPMB42ishOYpjDTn4LmXDNLM61Zspnuldmb7lrZEUzKVMD2uiMqO4ZRE26vquwIQoh/CDO9/AshhBBCCCGEeExW3IQQQgghhBBmQyObJY2SFTchhBBCCCGEMHOy4iaEEEIIIYQwG1pZcTNKVtyEEEIIIYQQwszJxE0IIYQQQgghnoDExETGjBmDr68vY8aM4dGjR0XaREVFMXLkSPz9/Rk4cCDLly8v1XPLxE0IIYQQQghhNjSV/KciQkJC6NKlC6GhoXTp0oWQkJAibVQqFVOmTGH79u2sW7eOn376iRs3bpT43DJxE0IIIYQQQognYM+ePQwdOhSAoUOHsnv37iJt3NzcaN68OQAODg7Ur1+fmJiYEp9bJm5CCCGEEEII8QTEx8fj5uYG6CZoCQkJxba/e/cuV65cwdvbu8TnlrtKCiGEEEIIIcxGZX+O27p161i3bl3e34cPH87w4cPz/j569Gji4uKK9HvnnXfKdJzU1FTGjRvHBx98gIODQ4ntZeImhBBCCCGEEHqFJ2qFLVu2zORjVatWJTY2Fjc3N2JjY3F1dTXaLjs7m3HjxjF48GB8fX1LlUu2SgohhBBCCCHMhraS/1cRffr0YePGjQBs3LiRvn37Fv33abUEBwdTv359xowZU+rnlombEEIIIYQQQjwBr732GkeOHMHX15cjR47w2muvARATE8Orr74KwKlTp9i0aRPHjx8nMDCQwMBADhw4UOJzy1ZJIYQQQgghhHgCXFxcjH4uW40aNfjxxx8BaN++PVevXi3zc8vE7X9A0PRXaO3Tjqz0TBZM+pbIixFF2vgGBeD/0mDUHu681nokyQ+TAbB3suf1uf+mRj01WZlZLHxvHnev3X4iuVx9vGk4cwwKlZKo1Xu49d2mAo/bNahJ02/ewrGlJ+Gz13Jnwea8x+q8PhD3F/oAWlKv3OHK+PloMrOfSC6AkdNfprVPWzLTMwmZNM9ozfoH+eP30iBqeLjzRusgUvQ1s3W0483/jqdqzeqoLJRsC/mdgz/vrXAmF5/WeM3Q1St69R7uzNtY4HHbBjVp/N+3cWjpSeTna7hrUK+arwTgPqIvKBREr9rNvR+3VThPrd6t6PzJSJQqJVfX7Of895uLtOn86Ujq9GlNTnomByeEEH8xEoB/Hfua7NQMtLkaNDm5/D7wIwDavPs0jV/oTUa8rpYn/7Oeu3vPlTlbv+kj8fJpTXZ6JlsnhRCjP64h5zrVCfzubWyqOBBzMZLNExagyc7F1cudgV+8Ro3mHhz84mdOhBSslUKpYPSWGSRHP2TDS19Wei6VtSUvrv8QCysLFBYqrm47weGvfy0xi0evVvhMH4lCpeTi2v2cmF90/Hw+GYmnj278dkwMIVaf11RfG2d7Bs0fi1Pt6iTdfcDmt74j81EaTYZ2pcPrA/Oet3rTOqwM+JAHl41fS/pOH0l9fZ22F1Onwd+9ja2+Tlv0dTLV37W+O4Pnjc3rX6WuG4e/2sCpJTvpPvEZGvRvi1ajJS0+ie0TF5ISm2iydvV6taL3dN25f3Htfv4wUrve+tplp2cSalC7/nNfpX7f1qTFJ7Gy/9S89g0HdqTLhKdxbVCTNUM+Jub8TZPHN6aPwVhtNzhe4ZoNmpd/bm17J79mxvqrrC157ucPUVlZoLRQcW3bCY5+lX9utRndnzZBvmhyc4nYe5aTs9aazFendyu662t2ec1+zhipWfdPRlJPf73Y824IcfoMQzfoM6hUhG87wR/6DB0nPYOnr27c0uOT2PPuQtJiTI/bYxUZP1N9A74fi0t9dwCsnezITEpjtX8wANWa1KHv7JewdrRFq9Hy0+CPyH2CP6+E+P+oop+l9v+VbJV8Qpo2bUpgYCADBw5kyJAhLF26FI1Gd9qFhYXRrl27vKXQwMBAjh49CkDjxo1577338p4nJyeHzp078/rrrz+RXK192qH2dGdCrzf5cep8Xp75htF2105eYdaLH/PgTmyBrweOfYZbl2/yvt87LHj3G4Kmv/JEcqFU0Pjzlzn3wmeE9ZiA21PdsGtUq0CT7MQUrgUv5faCgj9UrdQu1H7Fn5MDpnCi1yRQKnEb2vXJ5AK8fdqi9nRnYq+3WTz1B0bPfM1ou2sn/2T2i9OL1Kz/KH/uXb9LsP+7zBr+ES98GITKsoKvkSiVNJj9MhdfmMXJnhOo/lQ37BrVLtAkJzGFGx8uKTBhA7BrUgf3EX054z+VU30m4dq/HTae6grFUSgVdJ0ZROjIOfziM5n6gZ2p0rBmgTa1+3jj5Knm5+4TOfz+YrrOHl3g8W3PzmLjgOC8SdtjF3/cwcYBwWwcEFyuSVt9H29cPNUs7DWRHVMXM2DmaKPtek95jj8W7yCk9yQyHqXiPbw3ABmJqez6eCUnTExu27/kR9yN+2aTKzczmzXPf8YS/2CW+gdTv1crarbxKjaLQqmg78wgfg2aw7K+k2k8pDOuhcbP08cbFw81S3pOZNeUxfSbNbrEvh3fHsztI5dZ0msSt49cpuNbgwH4c+NRVvoHs9I/mO3vLODR3TiTk7bHdfqx10R2Tl1MfxN16jXlOU4u3sGP+jq10tfJVP+EiCiWBwSzPCCYFYM+JDs9k+s7TwJwYuFWlvl9wPKAYML3nKHr+KeKrV2fmUFsDJrDchO18/DxpoqHmqU9J7J7ymL6zMr/N1z++SC/jZpb5Hnjr95l82vfcDes7K/APh6rxT0nEjplMf0Njmeo59TnOLloB4t76WrWUl8zU/1zM7NZ/9xnrPALZoVfMJ69WuGuP7fqdGlKA992LB8wlWX9pnByoekXgxRKBT1nBrF11BzW9JlMw8DOuBSqWV0fb5w91azuMZH97y+m12f5GTYN/4z1A4JZ7xdM3d6tqKHPcOaHrazz/YD1fsFE7j5Dh2LGzTBLecevuL7b3p7Hav9gVvsHc2P7H9zY8Yeuj0qJ3zdvsueDpazoN4Wf/zULTXZOiTmFEMIYmbg9ITY2NmzatImtW7eydOlSDhw4wLx58/Ieb9++PZs2bcr707WrbqJhZ2fH9evXycjIAODIkSPUqFHjieVq178jh37ZD8CNM9ewc7KniptLkXaRl24Sdze2yNdrN6zDxSPnAbgffo/qtd1wruZc4VxObRuQdjOajFuxaLNzid14lOp+HQq0yY5LIvlsOFr9K8KGFColShsrFColKjsrsqIfVjjTY+36d+SwvmbhZ65hb6Jmty7dJO7ugyJf12q12DrYAmBjb0NqYgqanKL/hrJwbNOA9JvRZNyORZudw4ONR6g6oH2BNtlxSaScDUebU/CXAruGtUg6dR1Nehbkanh07DLVAjpWKE/11l4kRcaQfPsBmuxcIjYdp65vuwJt6vm248aGwwA8OB2OlZM9tm5VKnTc0mjYvx0Xf9Ed9/6ZcKyd7LE3ctx6XZvx57YTAFz45RAN9fnT4pOIPh+RtxJhyFHtilef1pxfu9+scmWnZQKgtFChtLRAW8L7qtWtvUiMjOGRfvyubj5Og0Lj5+Xbjsv6vFEGeYvr69W/HZc2HALg0oZDNPAteI4CNAnsyp+bjpnM1qB/Oy4ZHNfGRJ3qdm3GVX2dLhrUqTT963VrTuLtWJLuxQOQlZKe95ilnTXaYgpo7N/vZaR2V/QZoguN9b0TV8lITCnyvAk37vMwIsrkcYvTwLfgv9nUuVWnazOu6Wt2acMhGgxoV2L/AueWRf651XpkP8LmbyY3S3e9SYtPMpnPrbUXjyJjSNLX7Mbvx/EsVDNP33Zc1WeIOaO7XtjpM+SYyJBdeNxKcUOBioxfafoCNBrUiav6c7xez5bEXblD3BXdCxUZiSloNZV7m3Mh/gm0Wm2l/jFXMnH7C1StWpUZM2awevXqUg1+z5492b9/PwBbt25l4MCBxXcoA1e1K/H38z9nIiE6Htcaxm9Lasyty5F08O8MgJd3Q6rVqo6rulqFc1mrXcm8H5/398z78VirS5crK/ohtxdspuvpBXQ7H0JOUhoJB85XONNjLkZq5lKGmu1avo2aDWox74/FzN75NSs/WVLhi4C1e6F6RSVg5V61VH1T/7yDc+emWLg4oLS1wrVvW6xrVmwM7dxdSI3K/0DJtOgE7N0LTm7t1C6kGmROi0rAXq1vo9Xi99MUArfNoPGLPgX6NRvdn6d2fUaPL17FytmuzNkc1S4kGxw3OToBxxoFs9m6OJCZlIY2V7cqnhyVgKO66OS8sL4fj2DfZ2vK9YvXX5lLoVQwZtssxp2eT+ShC0SdDS+2vYPaheT7+eOXHJWAQ6EsDmoXkqMK5nVQuxTb166aE6n6LYapsYnYVXMqcuzGgzsVO3FzVLuQVI46OejrVJr+TYZ04crvBTP0eO9Z3jj2Dc2GduXwV7+YzFf4359SitqlROfn+yuYGitDhWuWYnBuFddfoVQwavss3jozn1uHLxCtP7dcPNXU7tiYFzdNZ/j6YNSt6pvMZ692IaVQzewL5dO1yc+QatBGoVTwrx2zGHN2PncOXSDW4PzuNPlZRoV9Q8OnunLiC9PjVqBW5Ry/0vSt1bExaXGPSIyMAcClvhrQ8tTKybywdSbt33hyP9+FEP97ZOL2F6lTpw4ajYb4eN3F/+TJkwW2St6+nb9NKCAggG3btpGZmcnVq1dL9cnppaVQKIp8rSxziN8X/IK9kwOzt33NgNEDibwUQW5uxVaP9MGK5irl7VctnO2p7teBYx3e5oj366jsbKgxrEfFM+VFM1az0hetZa823LoUydgOLxPsP5FRn76StwJX/lBGvlbKTOnX73F33iZarptGy5+CSbkUibaCK4DGAhWOU1wdtzz1KZv8P2TnyLk0DeqHulNjAK6s2M3P3d7lN99g0mIT6TTtxXJEK8X4leP7wquP7n1Jxt5vVZm5ALQaLUsDgvm+8zjcW3tRrdA22qJRjJxQhaMYHWNtqfqaom7tRXZ6FvHX7hYXzuhxS2qTl6GE/kpLFQ36teXq1rACbQ7N/Zkfuozn8sajtA3qX8Z8RRoVm+FJMzZWRr4hTTYprr9Wo2WFfzALO41D7Z1/biktlNg427M6cDoHZq1h8PyxRZ8j79Dlu14YZljvF8zyjuOo0doL18b553fYnJ9Z0Wk81387SsvRxYxb/oFKzGJy/ErRt3FglwIvTChVKmq2b8T2cfNZP+xTvAa0p0635iXnFEIII+TmJH8hwx/U7du3Z+HChUbbNWnShLt377JlyxZ69epV4eP2H+VPn+d0H+QXcf46VQ1WV1zVVXkYm2CqaxHpKeksfO+7vL9/eziEB3diKpwxMyoe65r5K0bWNauWerujS8+WpN+OJVt/A4sHW8Nw7tCImF8OlTtPv1F++Dyn+6Efcf5GkZolxpZ+K2avZ/uweb7uzfMxt6J5cCcWd69aRJy7Ue58mfcTCtbL3ZWs6NKPY/SavUSv0d0gxWPq82QavJpcHmlRCdi7569C2qldSSs0fqlRCdgbZLZzd827ccDj/8+IT+LWjlNUa+1FdNhVMuLyt1td/WkfvssmlipP21H98H5Ot3IXdT4CR4PjOqpdi9xoIj0hGWsnOxQqJdpcDY7urqTEFD/Gtds3okG/tnj19kZlbYm1oy2D/vsmW95ZUKm5DGUmpXH72BXq925FXDGTo+SoBBxr5o+fo7srKYXO8eToBBzdC+ZNjUlEZWlhsm9aXBL2blVIjU3E3q0KaXEFt881GdLZ6Gpbm1H9aKWvU/T5CJxqVuWewXHLUqfkqIRi+9fv7U3Mxcgi2R67sukow5ZO4oiJG7ykFKqdg7srqYVql1Kodg762j1JrUf1o9Xz+TUrPFYpMcXXzMGwZkbGunD/zKQ07hy/gof+3EqOesj17br3CEafi0Cr1WLj6khGQnKRrClRCTgUqllaofNa1yY/g7170ZplJaVx79gV6vZuRcLVguf3tY1HGbh8Ut6NS0ypyPgVPvcL91WolDTw68BPA6flfS05KoG7YX+S8VC3PTZy3zncWnhw58ilYnMK8b9OU8HPUvv/Slbc/iJ37txBpVJRtWrptrP16dOHOXPmPJFtkrtWbGdqwASmBkzgZGgYPYb1BqBBm0akJaeWaRJi52Sfd2ONPs/158qJS6QbvK+gvJLPhGNX3x2butVRWKpwG9qVOP2NAkqSeS8Op7YNUdpaAeDSoyVp1++V0Kt4u1fsIDhgIsEBEzkVeoLu+pp5tWlEWnJamWoWd+8Bzbu1AsCpmjPu9WsSe7tik93kszewre+OTV03FJYWVB/ajfjQ0tULwFK/Zc26VjWqBXTiwW9HKpTnwbkInDzVONSpjtJSRf3AztzedbpAm9uhp2nwTHcAqrf1Ijs5jfTYRCxsrbG0twHAwtaaWj1b8FD/S5jhe+Dq+bXP+3pJTq/YzdKAYJYGBHM99BQthumOW7ONF5nJaXnb9wrkO3aZJvr3+rUc1oPrhfIXdmDOeuZ3HseC7hP4/d/fc+vo5WInbX9XLltXR6yddFtKLawt8ejegvgSbp4SfS6CKp5qnPTj13hwZ8ILHSd812ma6fO6G+Qtrm/4rtM0f0a3+t38mR6E7zqV/4QKBY0GduLq5qITtzMrdufdOOR66CmaGzmusTo11tephUGdbuw+XWz/pka2Sbp45L+vuEH/tiSEm36vWfS5CFwK/fsjCtUuYtdpmuozqNt4kWXi31ARZ1fsZoV/MCv8g7mxs3Q1u3PsMo30NWv+TA/CQw3GzUj/wudWve4tSAjXnVs3Qk9St2szQLdtUmlpYXTSBhB7LgJnDzWO+po1GNKZm4VqFrnrNI31GWroa5YWm4iNqyNW+gwqG0tq92jBQ/357Wwwbp7925J4o+T3CFZk/ErqW7d7Cx6G3yfF4EW1WwfPU61JXSz078mu3bkJCRX8eSWE+N8lK25/gYSEBD7++GNefPFF49s/jHjmmWdwdHSkcePGhIWFldyhlM7sPUVrn3b89+APZKZnsnDSt3mPTV42jR8nz+Nh7EMGjB7I4Deeokp1F/6z8xvO7DvFj+9/T60GtXnzq/FocjXcu3GHkPfmFXO00tPmarg2dQmt1wajUCm5v2YfqVfvUnOUbtXr/opdWFV3pn3o54ReSWAAACAASURBVFjob6Fc57UAwnq8S9LpGzzYcpwOu/6DNjeXlAuR3Fu5+4nkAji79xTePm358uB8svQfB/DYpGXBLJo8n8TYh/iODmDQG0/hXL0Ks3d+zbl9p1n0/nw2fvszr3/5b2bv/BoUCtZ9vjLvowLKLVfDjQ8W02KNrl7Ra/aRdvUu7vp6Ra3YhWX1KrTd+TkqR1vQaKn16kBO9pxAbko6zRZNwsLVEW12DjemLiLnUWqF4mhzNRybthy/1ZNRKJVcW3eAxGv3aDKiDwB/rtrLnb1nqd3Hm2cPf0lORhaH3g0BwLa6E30XvQPothGFbzzKvf269yh2DH4O1+b1QKsl+U4cR6YsKXO28L1nqe/jzesHvyQ7PYttk0LyHnt22SS2T15ESmwi+2avJXDeWHpOepaYS5GcX7cfAPvqzgRtnoG1gy1ajYb2L/mxqN/7BW5gUR5/VS4HtyoM+up1FEolCqWCP7eEEb73bLFZtLka9k5bzrCVk3W3NV93gPhr92ilH7/zq/ZyU5/35UO6vDv1eU31BTgxfzODFvybFsN7kXQ/ni1v5F9vandqQnJUAo9uF72hj6EI/XFfPfglOelZbDeo07Blk9ipr9OB2WsZMm8sPfR1uqCvU3H9LWys8OjRgp0fFDyvek4Zjmt9d7QaLUn34gj9YGmJtXt65WQUKiWXTNTOw8ebMYd0GUINMvh/9zZ1ujTFxsWBV8K+5dhXv3Bp3QG8BrTH59NR2Lo6Erh0Eg8u3+K3kXOKrZVhzTx9vHlFP1Y7DI739LJJ7Hx/EakxiRycvZZB88bS/b1niS1UM2P97d2q4P/V6yhVunPr6pYwIvbozq0L6w7gN/c1Ru+aTW5WLtvfNb6j5HHNDk1bzuBVupr9ue4AD6/do7m+ZpdW7eXW3rPU7ePNi4d1Nds7MT9Dn691GVAqCN8cxi19hs5Th1PFyx00WpLvxnGgmHF7EuNnqu9jjYd05mqhFwUyH6VxetF2XtjyKVqtlsh957hZwvenEEI+DsAUhdacb53yD9K0aVMaNWpETk4OKpWKwMBAxowZg1KpJCwsjLfeeovatfP35b/55pv4+fnRpk0bzpw5U+C5wsLCWLJkicmtlY89X2/oX/JvqahXM6wqO4JRS2zM8xbMr2WqKjuCSdctrCs7glEPzLdkZsvSTK/0uaV7batSmG3NKjuACXZmWi+ATDM+zybcXlXZEYQwO4PrDqrU42++vaVSj2+KrLg9IVeuXDH5WKdOnTh16pTRxwpP2h6379Sp0xPLJoQQQgghhPhnk4mbEEIIIYQQwmyU9k7j/2vk5iRCCCGEEEIIYeZkxU0IIYQQQghhNuTjAIyTFTchhBBCCCGEMHMycRNCCCGEEEIIMydbJYUQQgghhBBmQz6tzDhZcRNCCCGEEEIIMycrbkIIIYQQQgizoansAGZKVtyEEEIIIYQQwszJxE0IIYQQQgghzJxslRRCCCGEEEKYDa18jptRsuImhBBCCCGEEGZOJm5CCCGEEEIIYeZkq6QQQgghhBDCbGhkq6RRMnH7B3NT2FR2BKPsVVmVHcEolUJR2RGMytaa78K3jZnej1erquwE/zzm+iPQTE8xwHy3pORWdgATHMx4MJVmOphBIR1J3/5tZccwytZ/XGVHEEIUIhM3IYQQQgghhNnQas315cbKZaavQQkhhBBCCCGEeEwmbkIIIYQQQghh5mSrpBBCCCGEEMJsyM1JjJMVNyGEEEIIIYQwc7LiJoQQQgghhDAbWllxM0pW3IQQQgghhBDCzMnETQghhBBCCCHMnGyVFEIIIYQQQpgNjXyOm1Gy4iaEEEIIIYQQZk5W3IQQQgghhBBmQ9bbjJMVNyGEEEIIIYQwczJxE0IIIYQQQggzJ1slhRBCCCGEEGZDI5sljZKJ2/+Apz8OoplPG7LTM1k9aQF3L0UWadNj1AB6veRPdQ81H7R5ldSHyQC4edXkhblvUKe5J1u+WMe+H7c8sVzOvdtQb8ZLKJRKYtfsJmrebwUer/pUT2q+PRSA3LQMIqeEkHY5EquaVfH6ZhyWbi5oNRpiV+0iZvHWJ5YL4MWPX8Lbpy1Z6Vn8OOk7bl26WaRNv1H++L40kBoe7rzdZjQp+poBNOncnBc+GoOFhQXJD5OYPfyjCmdy9fGm0czRKFRK7q/ey63vNhV43K5BTZp98yaOLT0Jn72W2wt0Y2Xn5U6LkHfy2tnWcyNizs/cCdlWoTzuvVvRfsZIFEolN9bs5/K8zUXatJsxklp9WpOTnsmxCSE8vBAJQJNX/fB6oTdotST+eZdjE0LQZGbT/YexOHq5A2DlZEdWUhrb+weXOVv/6SPx8mlNdnomWyaFEHMxskgb5zrVGfrd29hUcSD6YiSbJyxAk51L86Fd6fzGIACy0jLYGbyM2Cu3AQiY+yoN+rQmLT6JRb5T/9Zcrl7uDPriNWo09+DAFz9zwmD8Orzsh/dzvUGr5cGfd9nyXgi5mdkl5unzyUg8fXTjs31iCLEm8gyap8sTczGSbe/o8pjqr7K25LmfP0RlZYHSQsW1bSc4+tWvAHSb+AwNfNui1WhJi09i+8SFPIpNLDZjP4OabS2mZoHf5Wc0rNlAfc0OFqoZgEKpYPSWGSRHP2TDS1+WWK/H6vVqRa/pI1GolFxau5+T84ue+70+GYmHvjahE0N4oM/db+6rePbVnUOr+xc8h7xH98c7yBdNbi43957lyGdrjR7fo1cr+uiPf2Htfk4YOb6psTXV18bZnkHzx+JcuzqP7j5g81vfkfkoDZsqDgz5YRxq7/pc+vkgez5aAYCFjRVDFozDuZ4bWo2G8N1nuDprncma1ezdig6f5l8vLn5fNHOHT3XXi9z0TI5MCCHhYiROXu70XDA2r41DXTfOfbGBK4t24v3u0zR8oTcZCbpr75nP13Nv7zmTGUyp07sV3fQ1ubJmP2eN1LPbJyOpq7+W7Xs3hDj9uR644UOUVhYoVSoitp3gpP5cr9qsLj1nv4TK2hJNbi6Hg5cRezaizNkeO3LlFnN+PYxGq+Gpzs14qV+7Ao8v23uabSevAZCr0XIz5iH7Zr6Es70NK/ef5bfjl1GgoKF7VT55oQ/WlvLrnxD/RLJVspwWLFjAwIEDGTx4MIGBgZw7d46RI0dy4cKFyo5WQLPeranu6c7M3u+w9oMfeXbWK0bbRZy6yvwRs4i/+6DA19MSU/h1+jL2PsEJGwBKJR6fvcrVF2dyvvd4qgb2wLZh7QJNMu/EcHnYNC70e5d7X/+M55w3ANDmaLj16XLO9xrHpUFTqDHav0jfimjVuy1qT3cm9x7L0g8WEDTrNaPtrp36kzkjPuHB3dgCX7dzsmPUjFf57yuf84HvO8x764uKh1IqaPz5S5x9YTbHe7xLjae6Yd+oVoEm2YkpXA1exq0FBX/pSAuP4kTf93V/+k8hNz2LB9tOVCiOQqmgw2dB7HtxDlt6T8YjsDNODWsWaFOzjzdOnmp+7zaRsMmL6Th7NAC2ahcav+zLDv9pbO0zFYVSiUdgZwAOvzGP7f2D2d4/mDtb/+DOtj/KnM3LxxsXTzU/9JrI9qmL8Zs52mg7nynPcWLxDhb2nkTGo1S8h/cGIPHOA1b/ayaL/T7gyLcb8Z/9Ul6fCz8fZF3Q3DJnehK5MhJT2fXxSsJ+LDj5cKjhQvsxviwbNI1FvlNRqJQ0G9y5xDyePt64eKhZ3HMioVMW03+W8Tw9pz7HyUU7WNxLl6elPo+p/rmZ2ax/7jNW+AWzwi8Yz16tcG/jBcAfC7eyfMAHrPAPJnzPGbqMf6rYjPX1NVvYayI7pi5mgIma9Z7yHH8s3kGIiZqd+NH4ixTtX/Ij7sb9YjMUplAq6D0ziI1Bc1jZdzKNhnTGtdC57+HjTRUPNct7TmTPlMX0Majt5Z8PsnFU0XOodpem1Pdtx+oBU1nVbwqnFxrPrFAq6DcziF+C5rC072SaDOlM1ULHNzU2xfXt+PZgbh+5zOJek7h95DKd3hoM6MbzyJcbODDrpyJZ/gjZytI+k1nhH0yt9o2o6dPKZOZOs4LYM2IOv/tMxmNoZ5wLZa6lv15s7D6RY+8vppP+epEUHsUW32C2+Aaz1e9DctMzub39ZH49f9yR93h5Jm0KpYLuM4PYOmoO6/pMpkFgZ1wKZavr442zp5o1PSZy4P3F9PhsdF5tfh/+GRsGBLPBL5g6vVvhpj/XOwc/z8mvf2WDXzAnv/iFzh88X+Zsj+VqNMzecJDvXx/Er1NeYMfp64RHJxRoM7pPW9ZPfo71k59j3KDOtGtQE2d7G2ISU1hz8Dw/vfsvfpnyPLlaDTtOXy93FiH+Lhq0lfrHXMnErRzOnDnD/v37+e2339i8eTNLly5FrVZXdiyjWvi2549fDwJw68wNbB3tcKpepUi7e5ciSSg0aQNIiU/i9vkIcnNyn2guhzYNyIiMIvN2DNrsHBI2HcZlQMeCxz55ldxHqbr/Pn0NK/eqAGTHPiTtgu6VS01qBhk37mKpf+xJaOvbgSO/HgAg/Mx17BztcTZSs9uXbhJnpGadh/Tg1I4wEu7HAZAcn1ThTE5tG5B+M4aMW7Fos3OJ2XiUan4dCrTJjksi+Ww42mzTY+XaoyXpkTFk3I2rUJ6qbbxIjowh5fYDNNm53Np0nDoDCr4CXHtAOyI2HAYg/nQ4Vs722Ljp6qiwUKGysUKhUmJha0VazMMix6g7pBO3Nh4rc7aG/dtx8Rfdce+fCcfayR57t6LjV69rM/7UT2Av/nKIRr66/PdOXScjKU3X//QNHN1d8/rcOXGVjMSUMmd6ErnS4pOIOh+Rt9plSKlSYaGvp6WtFSlG6llYA992XNLniSomT52uzbimz3NpwyEa6Me5uP7ZaZm6XBYqlBYWPP44nqyU9LzntbSzhhI+p6c8NbvwyyEaGtQs2kTNHNWuePVpzfm1+4vNUFiN1l48iowhSX/uX9t8nPq+Bc/9+r7tuKLPHa3PbafPfd/EOdRyZD9Ozt9MblYOAOkmrhs1WnvxMDKGR/rj/7n5OF6Fjm9qbNTF9G3Qvx2XNhwC9OPs2x6A7PRM7v1xjZyMgiu4ORlZ3Dl2BQBNdi4xFyOxN/heMVT4ehFp5HpRZ0A7wvXXizj99cK20Firuzcn+VYsqffijR6nPNxae5EUGUOyPlv478fxKFRPD992XNPXM7bQeOYUOtfzft/TarFytAV0uwdSS/E9acrFW7HUqeZM7WrOWFqoGNCmIfsvFN0F8tj209fxa9sw7++5Gi2Z2Tnk5GrIyMqhurN9ubMIISqXTNzK4cGDB7i4uGBlZQWAq6srNWrUKNBmy5YtDB48mEGDBjF3bv6rq23atOHzzz/nqaeeIigoiIQE3atmt2/f5uWXX+bpp5/mhRdeIDw8/IlkrVLDlcT7+T/kHkUn4Kw2/sP172SlrkqWQa6sqHgsTfzQB6j+fD8S950p+jy1q2PXwpPU09eeWDaXGq7E38+f2CREx+OiLv3EUF2/JnbO9kxZ+wmfbJ5Dt6d7VTiTjdqVDIN6Zd6Px1rtUubnqfFUV2J+O1LhPLZqF9Lu57/imxaVgK17wTx2ahfSDDKn3U/ATu1CevRDrizYxtA/vuHps/PISk4j+sDFAn3dOjUm48Ejkm/GlDmbo9qFJIPjJkcn4FijYDZbFwcyk9LQ5moASIpKwNFIPVs915vw/efLnOGvzmUoJeYhYSHbePvYN4z7Yx6ZyWncPHSx2D4ADmoXkqMK5nFQF58nxSBPcf0VSgWjts/irTPzuXX4AtFn869n3d97lteOf0OzoV058uUvxWZ0VLuQXMaaJZeiZgB9Px7Bvs/WoNWU7ZVVB7ULyQbnfkpUAg6FMjmoXUgxqE2KkdoW5uKpplbHxgzfNJ1h64Op0ap+qY9fuCamxsaxmL521ZxI1W9bTY1NxK6aU7F5DVk72eHVrw1Rhy8ZfdxO7UJqoeuFnbqE64WRNp6BXbhZ6MWcJmP6M3jXZ3T98lWsnO1Knfkxe7ULKYVqYl/ouLo28UbbKJQKntkxi6Cz87l76AKx+nP9yPRVdA5+nhFh39Dlw+cJ+9z0NtKSxD5KQe3ikPf3GlUciNW/qFlYelY2R/+8Tb9WXnltR/m0xu+T5fT/aCkOtlZ0bVK33FmEEJVLJm7l0K1bN6KiohgwYADTp0/nxImC285iYmL44osvWL58ORs3buTChQvs3r0bgLS0NJo1a8Zvv/1Ghw4dmDdvHgDTpk1j2rRp/Prrr7z//vt88sknTyasouiXtObwafRGcplamXbq2gK35/tyZ9aKAl9X2tnQaNFkbn20hFyDV/Irns1o0UrdXaVS4dHSi6/GfMbcUTMY8u9nqeHp/uQzlfUpLFVU821H7ObjFX8uozUq0shIGy1WznbUHtCWTZ0m8Gubf2NhZ43H090KNKs3tAuR5VhtK+64JbUp3KRul6Z4D+/F/tnG32dUWbkKs3Gyo6FvW+Z3n8B3Hf+Npa01zZ/qVnwnQGH84lDqPMX112q0rPAPZmGncai9vajWKH8r8+G5PxPSeTyXNx6lzej+JYQ0dvyK18xL/z5FY++XK1Gpjlf2a4jCQom1sz3rAqdzeNYa/OePNdGw5OObHJty1KokCpWSQd+9zemlO0m5XXQHApT/emE41kpLFbV923JrS1je166u2M1vXd9ls28wabGJtP/oxXL8A0pRk2KyaTVaNvgFs7LjONxae+HSWHeuNx/Zl6OfrGZVp/Ec/WQ1vee+WvZsj49VutgAHLwYSWtPd5ztbQBISstg/8WbbP1oFKGfjiY9M4etJ6+WO4sQfxetVlupf8yVvDu1HOzt7fn11185efIkYWFhTJgwgYkTJ+Y9fuHCBTp27Iirq24FafDgwfzxxx/069cPpVJJQEAAAIGBgYwdO5bU1FTOnDnD+PHj854jKyur3Pm6j/Sly/N9ALh9LpwqNfNXi5zVriRVYMvGk5IVFY+VQS4r96pkF9qzD2DbtB6eX7zF1REzyHmYv71IYaGi4aL3iPv1IA+3hxXpV1Z9R/rR6/l+ANw8d4OqNavx+F0AruqqPIwpms2UhOh4kh8mkZWeSVZ6JldPXKZuUw9ibkaVO19GVDw2BvWyrlmVzOiyjWPVvm1IvnCTrAePyp3jsbSoBOxq5q+Q2rm7kl4oj65Nfma7mq6kxSSi7tGClDsPyNTfUODOtpNUb9+QyF91K4EKlZI6AR3Y7jet1HnajupH6+d8AIg6H4GTwXEd1a4kF7oBRnpCMtZOdihUSrS5GpzcXQtsL6zepA4B/3mF9UFzSS/n1si/IpcxHt1b8OjOA9L19by64yS12zXkkpGV1daj+tHqeV2e6PMROLoXzJMSU3weB4M8ydEJJfbPTErjzvErePRuRdy1uwUe+3PjUZ5eNomDX/9a4OttR/XD26BmjoVqllJCzRxLUbPa7RvRoF9bvHp7o7K2xNrRlkH/fZMt7ywoth/oV6kMzn0Hd1dSYwseLyU6AQeD2jgYqU3R533IDf17t2LORaDVarF1dcwb1+KOn1Lo+KbGRmlpYbJvWlwS9m5VSI1NxN6tCmlxpdvi7fv5yzyMjOb04p24mWiTGpWAfaHrReHt0UWuF+6upBvUrJaPNwkXIskwyGX439dX76PP8vyfw6WVGpWAQ6GaFM6ma1O1UJuC45mVlMb9Y1eo27sVD6/epdEzPTjy8UoAwreE0WuO8feXl0YNZweiDX7+xSSmUN3J+HbHHWcKbpM8fu0utVydcHXQbdvs26o+Z29GM7B943LnEUJUHllxKyeVSkWnTp0YN24c06ZNIzQ0tFzPo1Ao0Gq1ODk5sWnTprw/27dvL3e2wytDmRswhbkBU7gQepIOT/cEoF6bBmQkp5H0oPhfIP4OKWdvYOPpjnUdNxSWFrgGdudhaMEbUVjVqkajRZMJH/cNGREFJz2eX75N+vV7RIcUvftXeexZuYOPAibxUcAkToeeyNve6NWmIenJaTwqQ81Oh56gUYemKFVKrGys8GrdkPs37pbcsRjJZ8Kxq6/Gpm51FJYqagztStzOkyV3NKB+qhsxvx2tUI7H4s9G4Oipxr5OdZSWKuoFduZu6OkCbe6Gnqb+M90BqNrWi6ykNDJiE0m9F0+1tg1Q2eq2Gqu7N+fRjXv5OXu0IOnGfdKjSj9ZPr1iN0sCglkSEMy10FO0GKY7bs02XmQmp+VtATN069hlmgTo3lfZYlgPru/S5XeqWZVhC99h84QfSLgZXYaq/LW5TEm6H0/NNg2wsNHV06Nbc+IM6mno7IrdrPAPZoV/MDd2nqK5Po97MXnuHLtMI32e5s/0IFw/zuG7Thvtb+vqiLWTbsuahbUl9bq3ICFcdwOQKh75W8q9+rclIbzoixmnV+xmaUAwSwOCuV7Kmt02qFnLUtTswJz1zO88jgXdJ/D7v7/n1tHLpZq0gW5SVcVTjZP+3G80uDMRhY4Xses0TfW51frcaSXcPTMi9CR1ujYDoIqnGpWlRZFJ2+Pju3iqcdYfv8ngzoQXOr6psYkupm/4rtM0f6YHoBvnG7tOlViLbpOewdrRlr3TVxXb7vH1wkF/XI/AztwpdL24E3oaL/31olpbL7KT0kg3qJnH0KLbJA3fA1fXvz2JV8t+nY09F4GzhxpHfTavIZ2JLFTPyF2naaSvp1sbL7L042nj6oiV/lxX2VhSu0cLHupvdpMW85CanZsCUKtbcx5V4FrSvK4bt+MecS8+ieycXHaeuU6vFh5F2iWnZ3Iq/D4+LTzzvuZexYHzt6JJz8pGq9USdv0u9WuUfZu9EMI8yIpbOURERKBUKvHw8ADgypUr1KxZk+vXdWs0rVq1YtasWSQkJODs7MzWrVsZMWIEABqNhp07dzJw4EA2b95Mu3btcHBwoHbt2mzfvh1/f3+0Wi1Xr16lSZMmFc56ed8Zmvm0ZtqBb8hKz+Sn937Ie+z1pe+z5v0QkmIf0nO0H31fH4xj9Sq8v+M/XN53lrVTQnCs7syk3z/DxsEWjVZL75f8+az/JDIrujUxV0Nk8CIa//QRCpWSB2v3kH7tDm4jfQGIXRlKrQn/wsLFEY/Zurs6anNyueQ/GYeOTaj+bG/SLkfSYpfuFt53Zq/m0d7if1krrXP7TtPKpy1zD3xPZnomi977Pu+xd5cGs+T9+STGPqT/6AACXh+Kc/UqzNzxFef3nWbJlAVEhd/jwoGzzNzxFVqNlgPrdnPv2p0KZdLmarg6dQlt1n4AKiVRa/aTevUutUbpVgnvrdiNVXVnOoTOxsLRFq1GS53XAjjeYyK5Kekoba1w7dmSK5NCKpTDMM/J4OX0+WkyCpWS8LUHeHTtHg1H6lZ6r6/cy/09Z6nV15shR78kNz2LYxN0x44/E87trSfw3zkTbU4uDy/e4saqfXnPXS+wc7luSvJY+N6zePl488bBL8lOz2Krwb/5X8smsW3yIlJiE9k/ey2B88bSa9KzRF+K5Ny6/QB0G/8UNi4ODJgxGgBNbi7LBus+ziHw27ep26Upti4OvH38Ww59/Qvn1x34W3LZV3dm9OYZWDvYotVo6PCSHz/2e5/7Z8O5uu0EL22diSY3l5hLtzj70z4TKfJF7D2Lp483rxzS5dlhkOfpZZPY+f4iUmMSOTh7LYPmjaX7e88SeymSC/o8pvrbu1XB/6vXUaqUKJQKrm4JI2LPWQB6ThmOq5c7Wo2WpHtx7Jq6tMSa1ffx5nV9zbYZZHx22SS262u2T1+znpOeJeZSJOcNahZkULP2L/mxqN/7BW6SUlbaXA37py1n6ErduX953QESrt2j5QjduX9h1V4i957Fw8eboENfkpOexS6D3H7fvU3tLk2xcXHgpbBvCfvqFy6tO8CldQfoP/c1Xtw1G01WLqHvLjR5/D3TljNs5WSUKiUX1h0g/to9vPXHP7dqr8mxMdUXIGz+ZgYv+Dcth/ci6X48m9/4Nu+Yrx75GitHW1SWFjQY0J4NIz4nMzmDLuOGEn/9HqO2zQTgxtJd3Fiz32jmEx8up99Pk3UfB7BOd71opL9eXFu5l3t7zlKrjzdPHdHV7Oi7+TVT2VhRs2cLjr+/pMDztv3wOVyb1QOtlpS7cUUeLw1trobD05YzcJVuPK+uO8DDa/dopq/n5VV7ub33LHX7ePP8YV22/RN12ezcqtDn69dR6M/18M1h3Naf6wfeX6z7iAELJbmZ2RyYsrjM2R6zUCmZMqwHb/7wOxqNlsBOTWngXpWfj+jey/pstxYA7D0fQZfGdbC1tszr29JDTT9vL57/Yj0qpZImtasxrGvzcmcR4u9iznd2rEwKrTlv5DRTFy9eZObMmSQlJaFSqahXrx6ffvop48ePZ/LkybRs2ZLNmzcTEhKCVqulZ8+eTJ48GdDdnCQoKIiDBw/i4ODAf//7X1xdXblz5w7Tp0/nwYMH5OTkEBAQwNixJt7joDfe47m/459bZi9UYJvnX2m+VcXfJ/ZXGJVuvq+fRKssS25UCW6bZyyzZmmmV/oc8/y2BMDWTGtW8if0VQ63J3vz4ScqzUz3FwWFdCy5USWx9R9X2RHE/7CONSt+Y7eKOHG/dC/K/t1k4vY3a9OmDWfOFL07YnnIxK1sZOJWdjJx+/9DJm5lJxO3spGJW9nJxE0I4zrU7Fmpx//j/sFKPb4pZnopE0IIIYQQQgjxmEzc/mZParVNCCGEEEII8b/DfPdoCSGEEEIIIf7nyDu5jJMVNyGEEEIIIYQwc7LiJoQQQgghhDAb8nEAxsmKmxBCCCGEEEKYOZm4CSGEEEIIIYSZk62SQgghhBBCCLMhNycxTlbchBBCCCGEEMLMyYqbEEIIIYQQwmzIzUmMkxU3IYQQQgghhDBzMnETQgghhBBCCDMnWyWFEEIIIYQQZkMrWyWNkonbP5ibVlXZEYyKz7Wq7AhGdc0xz9M9WaGpgCdxmQAAIABJREFU7AgmXbc0zwunf3Z6ZUcwKiXXsrIjmPRIYZ7XC3OWrjTPTSmPzHQo08yzXACkKio7gXE/vXqisiMYla4AGFHZMUwae2dVZUcQolKY52+yQgghhBBCiP9JGvk4AKPM+PUxIYQQQgghhBAgEzchhBBCCCGEMHuyVVIIIYQQQghhNuTmJMbJipsQQgghhBBCmDlZcRNCCCGEEEKYDbk5iXGy4iaEEEIIIYQQZk4mbkIIIYQQQghh5mSrpBBCCCGEEMJsyM1JjJMVNyGEEEIIIYQwczJxE0IIIYQQQggzJ1slhRBCCCGEEGZD7ippnKy4CSGEEEIIIYSZk4mbEEIIIYQQwmxoK/l/FZGYmMiYMWPw9fVlzJgxPHr0yGTb3P9j776jorgev4+/dxcRkK5UK2AvIPaGEVBUDGJLbFGMRqMmGg0W1NhrrEk0MWKMFVuMJSQWVCyxF0TE3kBFiopIb7v7/LErdVGaX3jyu69zOJGdOzufzL17Z+/cmUEup1evXnz55ZeFem9xqeR/lNucodg5O5CRks7fk9YRFRqWr4xRdTN6r/4aXWN9okLDODDxFxQZchr1akfb0R4AZCSncmjGRmJuPwHgqzM/kJ6UilKuQCGX87vHzGJnrOLsQMMFXkhkUp76BfJo9V+5lleqbY39j6MxbGLDvcW7eLz276xlnS6vRp6UglKuQJkp52zXGcXOAVC9kz3t5g5BIpNyZ8dJgn/2z1em3bwh1HBpSmZKGicn+vIyxz6VSCX0OTifpKjXHB62AoDKDWvgtGQ4sooVUGbK+XfGJl4EPyp2RnNne5rMH4pEJiXc7wT31+TOqF/bmmY/fIlRk1rcXrKbB2v/AUDX2pRmq8egY2aMUqkkbGsgj347XOwcOXWfM5Q66na2f9I6IjW0M+PqZvRTt7PI0DD2TfwFeYY8a7m1vS1f7J/Lnq9Xc+vgJSrbWvHJmnFZy01qmHNi5R4u/F70zEadHKk5fzgSqZSYHceIXLMv1/LKvTti/VUvAOTJqYT5+JJ8Kwxt68rY/TieCuYmKBUKYrYdJXrDP0Xe/ruYOjtQd8EwJDIpz/0CCV99INdyvdrWNPxxDAZNbHi4eCdP1O1fz86Kxr4Tssrp1jTn0dI/eOp7sFRyWTjbY69uZ2F+J7inoZ01/+FLjJvU4taS3dzP0c5arB5DRTNjUCp5vDWQh6XUzspbLqtO9rSYPwSJVMqDHSe5tSZ/f9F8/hCqqvuL8xN9eX0jDID6I7thN6gTKJXE3XnG+Ym+KNIyMGlUg1ZLhiPVUfUXl6dt4lUx+ovqnezpMGcIUpmUWztOcu2X/Nk6zB1CTXW249+q+jJZxQr02vMdMm0tpDIZDw9e4vLKvQC0nNiHBoM6kfoqAYAL3+/myYnrRc7Vfo6qj7294yTBGnK1n5vdx57Ikctzz3dI1bkeHbzEFXWuyg1q4LT4cypU0iHh6QuOj19LRmLKe7PU+sgeZ3WW0J0nuaQhi/PcIdg4q7Ic9vYlRt23FbRux+kDsevsiDwjk7jwGI5M8iUtPpmaTo1x8umPtIIWioxMTi3cwdNztwq1z6p2sqfNXFVd3t1xkhANx6U284ZQXb3PTk/05ZU656fnV5Hx9lidKeevHrOy1mn4eRcaDHNDmSnnaWAwlxfuLFSet2p0ssdJvQ9u7ThJkIb955Snjb0IDUPfypTOP4xGz8wIpULJze0nCPn9CABVGtag02L18VIu5+SMTcSU4HgpCGXJ19eXtm3bMmrUKHx9ffH19WXy5Mkay27ZsgU7OzsSExML9d5lOuO2du1aevTogYeHB56enly//u4DwZAhQ7hx48b/KJ3m7Y4cOZL4+Ph3lv/1119z/T5gwIAPlk0TO2cHTG0sWfuRNwenbaDbgs81lnPxGcClDYdY28mb1DdJNO3fCYC4py/Y9ul8fus2jTM/7cd98Yhc620bsIDf3KeXaNCGVEKjJcO5PGgJp528se7dHv26VXMVyYhL5NaMTbkGbDld6DOfM64+JR60SaQS2i/w4uCQpex2nkJtzzYY17HOVaa6iwNGNpbs7ODN6akb6LB4WK7ljUd04/WD57leaz1jIFdX7eXPrjO4vOJP2swYWPyQUgkOiz/n/KClHO84mWq922GQZ3+lxyUS8t3mrAHbW4pMBaFz/DjecTKn3Wdh+3mXfOsWRx11O/vpI2/8p22gRwHtrIvPAC5sOMRqdTtzVLczUO37LtMG8PB0SNZrrx5F8qv7dH51n866j2eQkZLG7SNXih5QKqXWopHcHbyAkE7fUNnTCd061XIVSXsaza2+M7nR+VsiVv2BzdLRACgzFYTP20zIR+O5+bEPFsO651u3RKQS6i0ZTvCgxVxw+haL3u2ppKH9352xifC1ub8UJT+M5JLrVNVPFx/kKem8OHip1HI5LP6cs4OWcrSAdpahbmf387QzZaaCG3P8ONZxMidLsZ2Vt1wSqYSWi7w4MXgpf3eaQi3PNhjm6S+sXRwwtLHkr/beXJyygVbq/kLX0oR6I9w43H0m/7hMQyKVUsuzDQCO3w3kxsq9HOoyg5Blf+L4XdH7C4lUQscFXvwzdCk7XKZQx7MNJnmy1XBW9WV+Tt6cnLqBjxapssnTMjjQfxG7u85gd7cZ1Ohkj4WjXdZ6Ib8dZnc31bKiDtokUgkd1Ll2uaj62IJy7XDy5tTUDTjlyPVX/0Xs6TqDPd1mUL2TPebqXB8t+4KLS3bxR5dpPD5yhaajexQqi+sCL/Z6LWWT6xTq9WyDaZ4sNs4OmNSy5PeO3hz12UDnhcPeu274vzfY1MWHLV2n8/pxJK2+Up38TIlNYN/wFWxxm8ahievo/sPoQu+zdgu8CBiylD+dp2Cr4bhUTd3O/ujgzZmpG2iX57h08JOF7O86I9egzapdA2q4NWdfl2nsdfXhxq9FO+EjkUr4aIEX/kOXst1lCnU11GVNZweMbSzZ5uTNiRxtTCFXcHb+dra7TGWP5xzsvTpnrdtuxkAurdrLrm4zuLj8T9pPL8HxUhDK2PHjx+nVS3VSuFevXhw7dkxjuaioKE6ePEm/fv0K/d5lNnC7du0aJ0+eZN++ffj7+7Nx40YsLS1LdRtyubzAZZmZmcV6z/Xr12NoaPjOMuvWrcv1+86dRTubVVJ1uzQn5M9/AXh+7QE6hnromxvnK1erXSNuq7/whfx5mrpuLQCIuHqf1Phk1b+D7mNoZVrqGY2b1Sb5cRQp4TEoM+RE7j+HRbcWucqkv4znTfAjlBkF12NpMG9qR3xYNAlPXqDIkPPgwAVquTXPVaaWW3Pu7TkDQEzQQyoaVkJPvU8rWZlS07Upd7afzP3GSiXa+roAaBvokRT9utgZTRxrk/g4muQnqv31bP95LLvmzpj+Mp644EcoM3Pvr7SYON6oz/ZnJqWScD8CHUuTYmd5q16X5lxXt7Nn72hnNu0acUvdzoL/PE19t+x6bj2sK7cOXSbppeaTIbbtGxP7JIY3ES+LnE/fsTapYZGkPYlGmZFJ7IEzmHRtlatM4pW7yN8kqf4ddA9tq8oAZMS8JvmG6myvIimV1AfPqKBeVhoMm9Um5XE0qer2H73/HFW6tcxVJuNlPAnBD9/Z/k2dmpASFk3qs6LvH43v51ibpDztzCpPO0t7Gc9rDe0sNSaOuDztTLcU2ll5y1XZ0Y6EsGgS1f1F+IELVM+TpVrX5jxS9xevgh6ibVQJHfVnQ6IlQ6ajjUQmRUtXm2R1v6BUKqlgoOovKhjqkVKM/sK8qR1vwqKJf9uX/XUBmzx9mY1bc+7+qcoWfe0h2jn6sszkNACkWjKkWlqU1rMB8vaxD/8qoI9V54q5lruPzZvr7VVMxrZWRF64A8Cz06HYdM/9GdLEsqkdcWHRvFFnuet/gdp5sti5NeeWOkukOkslc+N3rhv+byhKuUK1TtBDDCxVx82Ym+EkRccB8OreM7QqVkCm/f6Lnczy7LNHBy5QI0/Omm7NeaBuZy+CVHWpq6EPzqn+kM6E/OyPIl31HSj11btPROdlkaeN3f/rArYa2tidHG3sbV0mx8TxQj0jmJGUSuyD5+ir9xNKJdrq9q9tWLLjpfDfoFAqy/Rn165d9OnTJ+tn165dhc7+6tUrzM3NATA3Nyc2NlZjuUWLFjF58mSk0sIPx8rsUskXL15gYmKCtrY2AKam2YOD8+fP8/333yOXy2ncuDFz587NKvfW7NmzuXHjBmlpaXTt2pXx48cD4OLiQp8+fTh79iyfffYZPXpkn4Hz8fHByMiIW7du0ahRI9zd3Vm0aBGpqano6OiwaNEibG1tSU1NZdq0aTx48AA7OztSU1Oz3sPFxYU9e/ZgamrK2LFjiYqKIi0tjaFDh9K/f3+WL19Oamoqnp6e1K5dmxUrVuDo6Mi1a9dQKpUsXbqUf//9F4lEwpgxY3B3d+fixYusWbMGExMT7t27R6NGjVi+fDkSiaRY+9bA0pT456+yfo+PisXAwoTEmLis13RN9EmNT8o60MRHxmKg4cuMw4BOPDyZ8+yqkkHbfFAq4Zrfca7tOFGsjDqWpqTmyJjyPBbjZrWL8A5KWu2aDkolT7Ye5+nW48XKAaBnZUJiZPaHKikqNuuM7luVLE1IypE3KTIWPUsTkmPiaDfnMy4s3EEF9SDtrXNztuHuN4U2MwchkUrY7zm32Bl1rUxIybH91MhYTIq0v1T0qlfBqHEtXgc9LHaWtww1tDPDPO1MT93OFDnamaG6nRlYmFC/aws2D1xI1aWjNG6jcc82hP51rlj5tC0rk54jX3rkKyo1q1NgebOBnYk7cS3/+1QzQ6+xDUlB94qVQ5O87T/t+SsMi1GfFr3bEb3vbOnlytPOUiJjMS1mOzNuXIvYUmhn5S2XrqUJyc+z+4vkyFgqN8vdX+hZmpCcI2/yc1V/ERvymNtrD9Lr8o/IU9OJPHWDqFOhAFydtQ2XHVNwnDUIiURCQM+i9xeVLE1IzJEtMTI216xZdpncfVkldV8mkUr45OACjGpZcGPzUWKCs/dTY68u1OvbgZiQx5yb70fam+QPmisxT66+6lyhOXLF3n1KLbdmhAUEYfdxa/St33+SUd/ShIQcWRIiY7Fqape/TGR2loSoWPQtTQq1LkDj/h25638x3+t13FsSczMcefr7TxzrWZmQlOO4lBwVi5lj/naW87iUrN5nKTFxoFTSbbsPKJXc8Qvkrp/qWG1ka4lF63o0n/oJ8rQMLs3fwcvrhb8ksVKefaCpLvU11KW+ui7fMqhWBbNGNYm6pqrLf+dso+e2KbT/TnW8/LNX8Y+XglAa+vfvT//+/QtcPmzYMF6+zH/SdMKECRpK53fixAlMTU1p3LgxFy/m7y8KUmYDt/bt2/Pzzz/TtWtX2rZti7u7O61atSItLQ0fHx82bdqEjY0NU6ZMYfv27QwbNizX+hMnTsTY2Bi5XM6wYcO4c+cO9evXB6BixYrs2LFD43bDwsLYtGkTMpmMxMREtm3bhpaWFufOnWPVqlWsXr2aHTt2oKOjg7+/P3fu3KFPnz4a32vRokUYGxuTmppKv379cHNzY9KkSfj5+XHgwIF85QMCArhz5w4HDhzg9evX9OvXjxYtVLMPt27d4p9//sHc3JyBAwdy9erVrGVFpWm8p8xz6lTjoDDP2dWabRvStH8ntvSdl/Xa5j5zSYyJQ6+yIYO2+fDyYSRPL90pRkhNLxb+9O75j2eTFv0a7SqGtNo9g8T7Eby+UIwcgERTmLxRNO9Uarg2JeVlPC9vhGHVtkGuxQ2HunJ+rh+PD17G9uPWfLR8JP8MXFKsjAVtvyhkehVp9dtEbszaSmYh7gN5fyZNkfJk0pD7bZFus4dwbMlOlArN/x+yCjLqdW7Ose8Lf5brffkKamKG7RpjPtCVW72m53pdqqdD3d+mED7rd+Slsc+yshXvpEyut6ggo4pbcx4u1NzXFes9S6mdtf5tIiGl1c7KWa7C9J0FfV61jfSo1rUZB1pPJD0+GSffcdTq056wvWep4+XK1dl+PD14mRoerWm9ciSB/YvWX2jKlv8jWfC+VCqU7O42A21DPbqvn4BpvWrE3n1G6NZjXPlxH0oltJ7cj3YzB3Ni0vqiBHtvLs1lsnPtUefqun4CJvWq8fruM05OWk/7eUNp/k1vwo4Goch4/4CoMPWn6ZigVCoLtW7rr3uiyFRwO88Jlcp1q9Jx2gD2fPb9ezO+TZE/Q54S79hnf/eeR3J0HDqVDem2YypvHjwn6uJdpDIpFY0q4e8xhypNbXFZ+zW7231byEwU0LbfXybnsaGCXkW6r/uGf+dsy7onsfEQV87M9ePhocvU/rg1LstGcmBQMY+Xwn9CSR8Q8qFt2rSpwGWVK1cmJiYGc3NzYmJick1OvRUUFERgYCCnT58mLS2NxMREJk2axPLly9+53TIbuFWqVIm9e/dy5coVLl68yMSJE/H29qZhw4ZUq1YNGxsbAHr37o2fn1++gduhQ4fYvXs3mZmZvHjxgocPH2YN3Nzd3Qvcbrdu3ZDJZAAkJCQwdepUwsPDkUgkZGRkAHD58mWGDBkCQP369alXr57G99q6dStHjx4FIDIykvDwcExMCr4E5+rVq/To0QOZTEaVKlVo2bIlN27cQF9fH3t7+6xLRevXr09ERESRBm7Nh3bBcYAzAM9DHmFonX1Zl6Glaa5ZEIDk2AR0DCshkUlRyhUYWpmSkOPSBPP61enx/Rfs9FpKSlz2DZNv3yf5VTx3j1zBuqltsQZuqZGx6OTIqGttSlpU4S+NSFNnTX8ZT/TByxg71i72wC0pMhb9HJeDVrI0JSlPlqTIWCrlyFvJypTk6Dhse7Siplszarg4IKtYgQoGurj8NIbA8Wup28+Jc7O2AvDo74t8tOyLYuUD1Yykbo7t61iZklKE/SXRktFqw0Se7j1L5MHLxc7RcmgXmqvbWYSGdpZQQDuTyqQo8rQza3sb+q3+GgA9UwPqODugyJRzJ+AqALU7NSUyNKzAyyjfJz3yFdo58mlbVSYjKv/lCroNamKzfCx3P5tP5uvsti7RklHnt8m83Hua14cKfzasMFIjX+Vq/xWtKxep/QNUdnUk4cZj0l8U/LSqosrbznSL0c7aqNvZ8xK0s/KcKzkyFr0cMzt6GrKoymTn1bNW9ReWTo1JfPqCtFjVQz6eHryCWYs6hO09i+0nTlydqeovnvhfpM3yovcXiZGxuWad9K1Msy7FzF0md1/29lK+t9Ljk4k4f5saneyJvfuMlByfwVvbT+C+ybtIuZIKkSspTy59dR+bN9dzda7Xd58R9zCSfwarBkJGNpbUdG363iwJkbEY5MhiYGVKYkzuLAlRsRjkuDTawFK1j2QVtN65bsN+Tti6OvLHwMW53k/f0pSevhM4NPFX3oTHvDcjqGfPchyX9CxNSX7PcUkvxz57+9/UV/GEH75KlaZ2RF28S1LUa8IOqe4Zfhn8CKVCiY6pAanqNvk+SXn2n76Vab7LGvO2Mf0cbUyqJaO77zfc23+OR4ez712u38+Jf2er2v+Dvy/isrT4x0tBKGsuLi7s37+fUaNGsX//flxdXfOV8fb2xttb1ZdevHiR33///b2DNijjh5PIZDJat27N+PHjmTlzJgEBAfnP2Gvw9OlTfv/9dzZt2oS/vz+dOnUiLS0ta7murm6B6+Zc9uOPP9K6dWv+/vtv1q5dS3p6etay912mePHiRc6dO8euXbv466+/aNiwYa4Mmrzr/y3npaAymeyd9+dpcnXLUX5zn85v7tO5F3AF+75OAFg71iYtISXfwA0g/PwtGrir7vmx79uR+0dVX5gNrSvTd90EDkxcS+zjqKzyFXQrol1JJ+vfth2b8OLusyLlfOvNtYdUsrVEt4YZkgoyrHq1I/rI1UKtK9OriEydQ6ZXkSqd7Em487RYOQBirj/CyMYSg+pmSCvIqO3ZhvCjQbnKhAcEUbdfBwDMm9mRnpBMckwcl5bsxq/leLa3ncixr37m+dlbBI5fC0By9OusWbiq7RvxJse+LKq44Ifo21qip95f1Xq1JSqgcPsLwHHVKBLvR/BwXcmePHh5y9GsB4fcCbiCg7qdVXtHO3t8/hYN1e2sad+O3FW3sx87TOSHDhP4ocMEbh28xD8zN2UN2gCa9GzLjWJeJgmQGPwAHRsrKlY3R1JBC1PPDrwOyP2lXbtqFer+NoWH438k9VFkrmU2K74i5X4EUb75n5hWUgnXHqJna4mOuj4terXjZREfwGLZuz3R+4q/fzR5raGdRRahnTVbNYqE+xE8KGE7K8+5XgU/wsDGkkrq/qKmZxueBeTuL54FBGGr7i8qN7MjPT6Z1Jg4kiJeUaVZbWS6qv7eskMj3jyIACAl+jXm6v7CokMj4ovRX8Rcf4RRrRx9Wc82PM7Tl4UdDaJeX1U2C8fsvkzH1ABtQz0AZDoVqObUOOuBS3o57puy6daC2CL2+3lz2fVsQ5iGXHXVucwLmUunsvp+c4mEZuM9ubnt/ZfMR11/hLGNJYbqLPU82vAwT5aHR4NoqM5i5WhHWkIySTFx71y31kf2tBrzMftHrCQzNfu7REVDPXpv8ubM97t5fuV+offZi+uPMLSxRF+9LVvPNjzJk/NJQBC11e3MrJkdGQnJpMTEoaVbkQrqY6SWbkWqdmzMa3WdhR++gnX7hgAY2lgi1dYq9KANIDpPXdbR0MYeHw2ivoY2BuCy7Ati7z8neP2hXOskRb+mahtV+6/WvhFxJTheCkJZGzVqFGfPnsXNzY2zZ88yapTqdpDo6GhGjhxZovcusxm3R48eIZVKqVWrFgC3b9/G2toaW1tbIiIiCA8Pp2bNmhw4cICWLXPfcJyUlISuri4GBga8fPmS06dP06pVKw1bebeEhAQsLCwA2Lcv+zHhLVu2xN/fnzZt2nDv3j3u3r2rcV0jIyN0dXV5+PAhwcHBWcu0tLTIyMigQoUKudZp2bIlu3btonfv3rx584YrV64wZcoUHj0q3UfePggMxs65KWNPr8z6cwBv9d80mX+mrCcxJo7AxTvovWYcH036hOib4QTvOgmA0ze90TUxoPt81VMC3z72v1IVQ/r5TgRUZ81uHjjHo1Mh+bZfGEq5gpvTNtJq53SQSXm24wSJd59RY2hnAJ5sOYa2mRHtAxahZaALCiW1RnXnX6dJVKhsQPONqrMUEpmU5/vO8rKITznLm+XMzM24+01BIpVyd9cpXt+LoMFnLgDc3hbIk8Bgarg4MODMCjJT0zn5re973/f0lA20mzsEqZaUzLQMTk/dUKKMIdM30W6Hj+rPAew4ScLdCGoNVZ3FCdtynIpmRnQ6siBrf9mN7MbxjlMwbFiDGp848ebWE5yPLQLg1uLdRB8Pftcm3+t+YDB1nJsyXt3ODuRoZ4M3TeavKetJiInj2OId9FszDpdJnxB5M5wgdTt7lwo62tg6NcZ/evH3GXIFYTN+o972WUhkUl7sPE7KvaeYD3EDIGZrAFUnfoqWiQG1Fqs6VWWmnJvdp6Dfqj5mn3Qi+VYYjY+q/rzD08V+vAkMKnBzRaGUK7g77Xcc1e0/csdJku4+o6q6/Ueo23/LgMVoGeiiVCipPsqdC07eyBNTkOpqY9qxCbcnvb8dFjVX8PRNtM/TzmzU7eyxup25qNuZUqGk9shuHO04BaOGNaipbmcu6nZ2sxTaWXnLpZQruDJjMy7bpyCRSXm48xRv7kVQZ4iqv7i/NZDnx4Op6upAz3MrkKekc36iqp5eXXvIk38u0f3IApSZcl6HhvNgm+reo4uTN9B8nurR7/K0DC5NLnrbV8oV/DtzMx7bVNnuqPuyRuq+7Oa2QMLVfdngMyvITEkn0FuVrZK5MS6rvkQqk4JUwkP/i4Sr91Hb6QOo0qgmKJXEP3vJKZ/fi5zrzMzN9FDnetvHNlTnupWjjx2oznVSnUtPnUsikyJR53qizlXHsy2NvFSfmceHrnB31+lCZQmcuZm+W6cglUkJ3XWKV/cisFdnCdkWyOPAYGydHRjx7woyUtI5ov6cFbQugMt8L7S0tejn5wNA5LUHHJu+kaZeXTCpZUGb8b1oM171lLk9n31PynseCqKUKzg/czPd1Mele7tOEXcvgvrqnHe2BfI0MJhqLg58oj4u/as+LumaGeL6m+o+G6lMxsP954g4qTpW39t1CqcVo+hzbDHyDDmnJ6zTHOAduU7P3Iynui5v7TpFrIY2VtPFgSHqujyurkurlnWp38+Jl7ef0P/wQkD1pyXCT1znxNQNOM3JPl6e8ClB3y/8JyiVirKOUGwmJiZs3rw53+sWFhasX5//MvPWrVvTunXrQr23RFmYKa4PIDQ0lAULFhAfH49MJqNmzZrMmzcPU1PTAh9OMmTIEKZMmUKTJk3w8fHh+vXrVK9eHW1t7ayHkuR8eEhePj4+dOrUiW7dugGqJ1v6+PhgYmJCmzZt+OuvvwgMDMz1cJIGDRrw5MkTZsyYQZMmTbLeX19fn7FjxxIdHY2NjQ2vX7/m66+/pnXr1ixbtozAwEAaNmxY6IeT/P7771lPo5w3bx6NGzcu8N66txbWHFz6FVMKHFM/7FMgi+tphfL5ZwstMstv5xRcseT3Yn0I3TNK8X6zUpQor/D+QmXkjURW1hH+v5NShCd9/S+9KadVWT73lkpS+ezKMC6n3X9KOd1fb339dFtZRxA+MJvKDmW6/cevij8h8CGV2cBNKDkxcCsaMXArOjFwKxoxcPtvEQO3oimfe0tFDNyKRgzchLJWs7J9mW4//FXxrij70MpzPysIgiAIgiAIgiAgBm6CIAiCIAiCIAjlXvm8dkwQBEEQBEEQhP+TxJ1cmokZN0EQBEEQBEEQhHJOzLgJgiAIgiAIglBuKBAzbpqIGTdBEARBEARBEIRyTgzcBEEQBEEQBEEQyjlxqaQgCIIgCIKBxoBkAAAgAElEQVQgCOWGeDiJZmLGTRAEQRAEQRAEoZwTM26CIAiCIAiCIJQbCjHjppGYcRMEQRAEQRAEQSjnxMBNEARBEARBEAShnBOXSgqCIAiCIAiCUG4oxd9x00iiFI9t+f9W0mKvso6gUY+fI8o6gkaHtn9W1hE0avfp+rKOUKDvldXLOoJGQ1KDyzqCRimZ6WUdoUDp8syyjqCRsU6lso5QoH7G9mUdQaM4ZUZZR9DIVqJb1hEKZKSQlHUEjYIlyWUdQSNTiXZZRyhQLUX5nXOY+GRbWUf4z7A0blCm24+Ku12m2y+IuFRSEARBEARBEAShnCu/py0EQRAEQRAEQfg/R1wQqJmYcRMEQRAEQRAEQSjnxIybIAiCIAiCIAjlhkI8nEQjMeMmCIIgCIIgCIJQzomBmyAIgiAIgiAIQjknLpUUBEEQBEEQBKHcEA8n0UzMuAmCIAiCIAiCIJRzYsZNEARBEARBEIRyQyFm3DQSM26CIAiCIAiCIAjlnBi4CYIgCIIgCIIglHPiUklBEARBEARBEMoN8XASzcSMmyAIgiAIgiAIQjknZtwEQRAEQRAEQSg3FIgZN03EwO0/TmbbBO3Og0EqJTP4FBkX/slXRlqjPtqdByGRaqFMSSDVbzEA2u4j0KrdFGVyPCm/zfgg+cbN+4o2Lq1ITUljycSl3A99kK/MjNXTqGdfF3lGJreD77LCZxXyTDmVDCox4ycfzKuaI5PJ2LXuDw7vPlLiTGdDH7J05zEUCgW9nZoyvHvbXMsTklOZscGfqNh4MuUKhnZtTa/29qRlZDJ86TYyMuVkyhV0bl6PsZ4dS5wnpykLJtDetS2pKanM/mYhd27cy1dm9kofGjrUB4mEJ4+eMmv8QlKSU7KWN2xany3/+OLz5SyO/X2yVHJVdnag/gIvJDIpz/wCCVv9V67lerWtafzjaAyb2HB/8S7C1/6dtUzLUI9GK79Ev341lEq4OfFX3ly5Xyq5ABZ+PwNXt46kJKcyfuw0bly/VWDZRUu/Y8Dg3thWbQ5Auw6t2Lz9Z56EPwPgH/+jrFz6S6llW7psFm5dO5GcksqYLydzPfhmgWWXLZ/N4CH9sLZoAoCxsSE/r/0eG9uapKWmMXbMVG7fyt8eimPFirl06+ZMcnIKI0d6Exwcmq/M+vUrcHJqzZs3CQCMHOlNSMgt6ta1w9d3OY6OjZk9exk//OBbKpkAFnw/HdcuHUlJSeWbsdPfWZcLl85gwKDe2FVrAUC7Di3Z5PczT56o6vKg/7FSrct+s4fRyNmR9JQ0tk5ay7Obj/OV6Ti0K87D3TGrZclUxy9Ieq3ady08O9BldE8A0pJT2fXdBiJuh5dKriFzRtDUuRlpKWn4TlpDWOijfGW6eHWn2/CPsahlxeimXiSqc+ka6DHmh2+obG2GTEvKQd+/OP1HYLGzuM8eSh1nBzJS0tk3aR2RN8PylTGuZsana75G10if5zfD2DvxF+QZcmq1acAg3295/ewFALcPX+bkT/uy1pNIJYz2X0B81Gv8Rix/Z45aH9njPGcIEpmU0J0nufSLf74yznOHYOPclMyUNA57+xITGvbOddt596O2WzOUCiXJr+I57L2OpOg4dIz18fh1PJYOttz84zSBs7YUc++p6tIhR12Ga6jLzjnqckyOunT/0pN26uORTEuGde2qjHX8nKQ3icXOU5A+s71o6OxIRkoafpPW8kxDPTsN7cpHw7tjVsuS6Y4jsz4LxVHzI3s6zRmCVF0nlzXUZyd1fWakpBGQoz4LWtesYQ1cFw1HVrECSrmc4zM2EX39EVItGV2WfoF541pIZFJu7z3D5Z/zb08QPhQxcPsvk0jQdhtK6s6lKONj0Rk2h8z711C+ep5dpqIeFbsOJXXXcpTxsaBnkLUo88YZMq8eo6LHqA8Sr7VLK6rZVGVwBy8aNmvAxMXfMNZjXL5yx/YdZ+E41WBy5prp9Bjozl9b/enl1ZOw++FM/3wmRqZGbD29kWP7jpOZkVnsTHKFgsXbA/h14gAsTAwZvHATHznUwc66SlaZXSeCsLWqwk/jPiE2IZle362jR+tGaGvJWO89CD0dbTIy5Xy+dCsdGtthb1e12Hly6uDalhq21fBs258mzRox/ftJDHXPXzfLZ/1EUmIyAN5zxjFgeF82rtkGgFQq5ZvvxnL+5KVSyaR6UwkNlgzn6qcLSX3+ijZHFvHiyFWS7kVkFcmMS+TOjE2Yd2+Zb/X6C7x4eSKY61+sQlJBhky3YqlFc+3SERu7mrRx7ErzFg4sXTmb7q79NZZ1cGyMoZFBvtcvnr/KZ/1Hl1qmt9y6dsKudi2a2rvQsmVTVv0wH5dOfTSWdXRsgpGxYa7XvCeP5UbIbQYPHEOdurasWDWPnj0+K3Gurl2dqV27Fo0adaRVK0d++mkhHTt6aiw7bdoi9u07mOu116/j8PaeTc+eXUucJSfXLh2xta1J22bdaNbCge9XzMK98wCNZR2aNsLQyDDf6xfPX2XIgDGlmgugYaemmNlYMrfTN9RyrMOAhSNY3uu7fOUeXb1LaGAQ3+yclev1V09j+KH/XFLik2jYqSkDF4/UuH5ROTg3w9LGCu+PvsLOsS7DFoxiTi+ffOXuXbnDteNXmLFzfq7XuwztTsT9Z6wcsRgDU0OWnVjN2f2nkRejj63TyYHKNpb82Mmbao618Vj4Ob69Zucr5+YzgHMbDhHqfwGPhcNp1r8Tl7cdByD88t0CB2VtP+/GiwfPqaiv+84cEqkE1wVe7Bm8hITIWAb7z+PB0avE3s8+Lto4O2BSy5LfO3pj5WhH54XD2O45553rXln3D+dW7AHA8XM32n7Tm2PTN5KZlsG5FXuoXK8aVepWK/J+e8vBuRkWNlZMUtfl5wXU5f0rdwg+foXpeery4LoDHFx3QJXPtQXdvvD4IIM21WfBigWdJlDTsTafLPyCVQV8Fm4GBvF1ns9CUUmkElwWeLFXXSeD/OfxME991nJ2wLiWJRs7emPpaIfLwmHsVNdnQes6TR/IhR/2EnYyhFrODjhNH8ie/gup06MVMm0ttrpNQ0tHm6HHv+fugfPEP3tZov8PQSis/8Q9bvXq1WPJkiVZv2/YsIHVq1cX67327t1LdHR0gcu/+uorPD096dKlC82bN8fT0xNPT0+CgoKKtb0PSWpti+J1NMq4F6CQI799Ea26zXKV0WrUhsy7V1WDNoDk7LNeiqd3UaYmfbB87d3acWTPUQBuBd1G31AfU3PTfOUuBmYPMm4H38XMSjWIUipBr5IeALqVdEmIS0CeKS9RptDHz6luZkI1MxMqaMno2rIBJ4Nzz2JIJJCUloZSqSQlNR2jSjrIpFIkEgl6OtoAZMoVZMoVSCQlipPLR1078PfuwwDcCLqJgaEBVcwr5yv3dtAGUFG3IsoclxsMGNGP4/+cJPbl61LLZdSsNsmPo0gJj0GZISdq/znMu7XIVSb9ZTzxwY9QZuSuH5m+LiZtGxDhdwIAZYaczPhkSku3Hq78sUP1ZeXqlesYGhlibmGWr5xUKmX2vMnMm/XuM/Wlyb1HZ3ZsV80YXL4cjJGRIRaWmrPNX+jDzO+W5Hq9fv06nDx5DoD79x5Rs0ZVzMyr5Fu/qDw83PDz+xOAS5euYWxsiKWleaHXf/HiFVevhpBRghMomnR1d2H3TlVdBr2nLmfNn8z8/2Fd2ru15NLe0wCEXbuPrkElDM2M85V7djOMWPWMUU6Pg+6REp+k/vd9jC3zf66Lo3mXVpz58yQAD6/do5JhJYzNTfKVC7/5mJcacimVSnTVAyGdSjokxSWiKGYfW9+tOcF7/wXg2bUH6Bjooa9hH9m0a8Stg6o+P/jP0zRwa5GvTF6GlqbUdWnK1Z0n3lvWsqkdcWHRvHnyAkWGnLv+F6jt1jxXGTu35tz68wwAkdceUtGwEpXMjd+5bnpi9lUNFfQqZj1YITMljYjL95CnZrw327s0y1OXeoaVMCpCXebUxrMD5w/8W6I8BWns1oLL6s9C+LUH6BroafwsRBTwWSgqTXVip6E+b6vrM+o99fl2XaVSibaBqu1XNNAjKVp9zFSq6lcik6Klo40iI5O0hBSE0qdUKsv0p7z6TwzctLW1CQgIIDY2tsTvtW/fPmJiYgpc/vPPP3PgwAEWLFhAixYtOHDgAAcOHKBZs2YFrlNWJPom2QMyQJkQi8Qgd0cvNbVEoqOHziAfdIbNRatx+/9ZPjPLKrx4nt1xv4h8gZllwV88ZVoy3Pp25tLJywDs27SfmnVq8OfVXWw8tp7Vs34p8YctJi4RS9PsM/UWJgbExOW+hGOAS3MeR76iy+TV9Jv7G5MHdEEqVY3Q5AoFn87dgIv3j7RpYEMT29KZbQMwtzIj6nl224yOjMHcKv8XV4A5P0zn2A1/atWuyc4NqrPAZpZVcHHvyJ7N+0stE4COpSmpz19l/Z76PJaKlvkH4Jro1TQn/VU8jX4cQ5tji2m4chQyvdKbcbOysiAiIjLr98jnUVhZW+QrN2LUYI4cCiQmOv8XieatmhJ4Zj/b9/hSr37tUstmbW3Js2fZ2SKeR2FtZZmv3Jejh3Lo4HGio3Jnu3HjNj09VbNazZvbU71GVapa51+/xLkiorAu4H3nzp3M5ctHWLp0Ftra2iXe9rtYWVnwPCIq6/fI51FYWeUfUA4fNZgjh04UWJfHz+xj+x/rSrUujS1MeJ3jMxAX9QrjQn4G8mrX35lbJ4NLJZeJpSmvnmfPBMRGvcLEovC5jm4+iHXtqqy5vIHFR1axde7vxe5jDS1MeZNjH8VHxWJomft4pGeiT2p8Egq5AoA3kbEYWGSXqd6sNmMPLWLIpimY1cnuW7vPGsKRxTsKlU3f0oSE59nHxYTIWPQtTPKXiczOmhAVi76lyXvXbT/5E0Zd+JEGvdpxbsWf781SFCaWpsTmqUvTItTlW9o62th/5MjlQxdKM14WYwtT4nLU85uoWIyK+VkojLx1kliI+kwsoD5zrntq7jacpg/kiws/0vG7gZz5fhcA9w9eIiM5jVFX1vDFhR+46nuQtDcf7gS3IOT1nxi4aWlp0b9/fzZv3pxvWWxsLOPGjaNv37707duXq1evAjBmzBj271d9gd25cyfe3t4cPnyY0NBQJk2ahKenJ6mpqUXK4eLiwsqVK+nfvz99+vTh5s2bjBgxgs6dO7Njxw4AkpKS8PLyonfv3nh4eHDs2DEAQkJC8PDwIC0tjeTkZHr06MG9eyW8X0XTdE/eA5tUhtSyFql/rCR11zIqtO+JxDT/F9sPQkO+dx14Jy76hpCLIdy4pLrfplWnFjy4+ZC+zfvzRdcv+WbB1+jp65UokqbtS8id89zNx9SrbsHRZePYNWs4S7YHkJiSBoBMKmX37BEcWfo1oWHPeRBR8jOKWTmKsL/mTFiEm4Mnj++H4ebpCsDk+d/w4/y1KBSKUsukCqbpxcJ9uZNoyTBoYsOzzUe50Hka8uQ0ao3TfFleaWXLu88sLM3x6NWN39Zty1c25PpNmjd2waVDLzas28am7WtKL1oh6tPS0pxevd35dW3+vm3Vil8xNjbizPm/+XKMFyHXb5EpL/ksl+ZuI399zpz5Pfb2zrRv74GpqTGTJpX+JYi5c71/f1lYmuHh2ZUNGuvyFi2auOLaoTcbfP3Y6Fd6dVnUvqwgddo2om1/Fw4s8SuNVEXqMzRp8pEj4TfD+LrlCGZ092bovC+yZuCKniX/a/myaCyk+k9kaBgr23/DL92nc2HTEQb5fgtAXRdHkl69IVJ9z9L7cxS8jawyGjoOpVL53nXPLvsD3zbfcHv/ORyHdSlUnsIqaV2+5di5Jfev3Pkgl0kChepzS3d7mvZLvkIayijfua79EFdOzfPjtzbfcGqeH27LRgJg2dQWhVzB+pbj2ND+W5qNdMeohuYTqELJKJTKMv0pr/4z97gNHjyYnj178sUXX+R6feHChXh5edGiRQueP3/OiBEjOHToEPPnz2fgwIFUq1aNjRs3smvXLoyNjfHz82PKlCk0adKkWDksLS3ZtWsXixYtwsfHhx07dpCenk6PHj0YOHAgFStW5Oeff0ZfX5/Y2Fj69++Pq6sr9vb2uLi48MMPP5CamkrPnj2pW7duifaJMiEWiWH2mS6JgSnKxLjcZeJjkScnQEY6ZKQjf3oXqXkN5LEFXy5aEr28evLxIHcA7ly/h5l1dodnZmXGy+hXGtfzmjgEY1MjZk5dlfVat0+7sf1n1YA4Iuw5kU+jqFG7OneC7xY7n4WJAVGx8Vm/R79OwMxYP1eZA2dDGN6tLRKJhBrmplStYszjqFc0sbHOKmOop0OLujU4G/qI2lWL36l/+nkf+gxWPbTgZvBtLK2zZxgsrMx5EVXwdfUKhYKAA8cZOnYQf+08SEOH+ixZNxcAY1MjOri2JTNTzsnDJbtkJjUyFh3r7Eu7dKxNSYsq3KWYqc9fkfY8ljdBqofSRPtfxGZczxLl+fyLQXzm9QkAwdduULWqVdYyK2tLoiJzz6g3sW+AjW0NLlwLAEBXT5cL147QxrEriQnZZ1KPHz3NkhWzMTU1JjY29+eosEaOGoLX56p77IKuhlCtWna2qtaWREbl/tw5ODTC1q4mwTdUl4Dp6ekSHBJIU3sXEhISGTt6SlbZG7dOEx72rFi5vvxyKMOHDwTgat5cVS2JjMzfH0RFqfZjeno6W7bsZsKEL4u17Xf5/ItBDPbqB0BwUCjWVbNn/qysLYnKMwvZxL6hui5VDynS1dPlfNBh2jbrpqEuZ5WoLjsOcaPdQNVJkfDrDzHJ8RkwtqzMm+iiXY5sXb8Gg5aMYu2wJSTFFf9Ldeeh3XAeoBo0PAp5QOUc9+eaWlYmLqbwuT76xAX/X/YCEB0exYunMVjZVeXR9fwPkdKk1ZAuNB/oDEDE9UcY5dhHhpamJETn3vfJsQnoGFZCKpOikCswsjIlQZ03LceliPdPXke6QIaeiT41WtSlXufm1HFuilbFClTU16XvqjH8OXGtxkwJkbEYWGcfFw2sTEnMs08SomIxsMrOamBpSlJ0HLIKWu9dF+D2/nP02TSJcyv3vncfvUvnod3olKMuTfPU5esi1OVbbTw6cP6vMyXKlVeHIW60HegCwJPrDzHOUc9GlqbEF/GzUBSJeepT38qUpDz7JTFPfeoXUJ85123Y14mTs7cCcO/vi3T+XvXdsp5nO8JPhaDIlJPyKp7nV+5hYW/Lmyeld5JWEN7lPzHjBqCvr4+npydbtuR+YtO5c+eYP38+np6ejBkzhsTERBITE6lSpQrjx49n6NChTJ06FWPj/NdgF4erq+pAXrduXRwcHNDX18fU1JSKFSsSHx+PUqlk5cqVeHh48PnnnxMdHc3Ll6ov31999RVnz54lNDQ03wC0OBTPHyM1sUBiVAWkMmQNWpN5/1quMpn3g5BVrwsSKWhpI7O2Q/HyeQHvWHL7N//FF11H80XX0Zw5fJau/VQHpYbNGpCUkERsTP7LXXsM7E7Lj1ow7+uFuc7cxUTE0LyD6hJVkyrGVLerTmR4ZL71i6JRLWuexLwm4kUcGZlyjly+zUcOdXKVsTI15OKdMABexScRFv2KalWMiU1IJj5ZNUubmp7Bxdth2JTwEpHdG/cyoPMwBnQexonDp/n4024ANGnWiMSERF7G5B/oVq+VfQlRR7f2hD1QPZ3u41af0KNlP3q07Mexv0+y2Gd5iQdtAPHXHqJna4luDTMkFWRY9mpHzJGrhVo3/cUbUp+/Qs9ONVCo7NQ410NNimPjb9txdeqNq1NvDv19nE8GqmbwmrdwICE+Id8ldMcCTtGkrhMt7V1pae9KSnIKbRxVlyDmvGfMsVkTpFJJsb/oA6z33UqHth/Toe3H/ON/lIGDegPQsmVT4uMT8l0OeeTICerYtqZJw440adiR5OQUmtqrviAZGRlQoUIFALyG9efc2UskJBTvC/+6dVto3bo7rVt356+/jjB4cF8AWrVy5M2bhKxBWk4573vz8OjKzZvFP2FSkI2/baezUx86O/Xh8D/H+XSAqi6bvaMu7et1pKV9Z1radyYlOYW2zVSfmbx1KZGUrC5Pbw1giftUlrhPJSTgMq36qJ7YV8uxDikJycS/KPx7m1hXZuSv3myZ+DMxj0vWhx3bcpgZ7t7McPfmasAlOvTtBICdY12SE5KLNHB7GfGCRu3tATCsYoSVrTUxTwp/Uu/S1qOsdZ/OWvfp3Am4QtM+TgBUc6xNakIKiRr20ePzt2jo3gqApn07cjtA1ZfomxlllanqYItEIiH5dSLHlu5iRdtxrOowgT/GreHxuVsFDtoAoq4/wtjGEsPqZkgryKjn0YaHR3Pfo/7waBAN+3YAwMrRjrSEZJJi4t65rnGt7CtVandpRuzDktUjqOryO3dvviugLt8UceCma6BH/TYNCQooxYdTAWe2BrDM3Ydl7j7cCLhCS/VnoaZjbVKL+FkoqqjrjzDJUyeP8tTno6NBNFDXp6WjHek56rOgdROjX1OtTQMAqrdvRFyY6jLthOevqN6uEQBauhWxalab2Acf7juTIOT1n5lxA/Dy8qJPnz706ZP9ZDaFQsGuXbvQ0dHJV/7evXsYGxu/8562onr7RUoqlea650MqlZKZmYm/vz+xsbHs3buXChUq4OLiQlqa6jK7N2/ekJycTGZmJmlpaejpleyyP5QK0o9uRWfAZJBIyQw5jfJlBFqOqjOgmddOoHwVifzRDXS/WABKJRnXT6F8qfriXNFzDNIa9ZHo6qP71Soy/t1HZsjpkmXK4ULgRVq7tMLvzBbSUtP4/ttlWcuWbFnIsskreRX9im8XTyDqWTS/HPgJgNOHzrDlh21s+XEbPisn8/ux9UgA30XrefM6voCtFY6WTIrPoC6M+WEnCqUSz/b21K5qxh8nVZ35J52aMfLj9sza+Df95vyGUqlkQl9nTAz0uPcshpm//41CoUChVOLWogEd8wz6SuLMsfN0cG3LXxd2k5qSypwJi7KWrfZbzrxvl/Ay5hXzfvqOSgaVkEgk3Lv5gEVTl73jXUtOKVdwZ9pGmu2cjkQmJWLHCZLuPqPa0M4APNtyDG0zI9oELELLQBelQknNUd056zQJeWIKd6ZvpMkvXyPV1iIlPIbQb34ttWzHAk7h6taRi8EBpCSn8s1X07OW+f2xjm/HzSRaw6DkLQ/PrniNGIA8U05qaipfDvcutWxHjpzArWsnrt84QXJKKmO/zJ4927P3d74e66NxwPRWvXq1Wbd+BXK5nDt3HvD12Kmlkuvw4UC6dXPm1q1/SU5OYdSoSVnL9u/fxJgxU4mMjGbTph+pUqUyEomEkJCbfP21at9aWJhx9uzfGBrqo1Ao+PrrETg6uhZ7UPnWsYBTuHbpyIVrR0hJTmVCzrrcvY5vx3+Xb+Cbk4enG17DB5IpzyQ1JY3RI0qvLm+euEYjZ0dmn/qRjJR0tk3OHjiM2ejD9qnreBPzmo+GdaPzlz0xNDNm+uGl3DwRzHafdXQf349KJvr0XzACAEWmnKU9pxe0uUILDryKg3MzVpz+hXT1I+TfmrRpBr9N+YW4mNe4DXPn49G9MTIzZvGRVVw/EcRvU39h/09/8OWKcSw+sgokEnYt2Zr1ePmiuncimDrOTZlwaqXqzwFMXpe17LONkzkwdT0JMXEcXbKDT1aPw9X7EyJvhhO0+yQADbu3otVnnVHI5WSkZvDHuOJd6qqUKwicuZm+W6eoHgG/6xSv7kVg/5nqhEjItkAeBwZj6+zAiH9XkJGSzpFJvu9cF8DJpz+mdlYoFUriI15ybNrGrG1+cXYV2ga6yCpoUbtrC/Z8tiTXUw8L43rgVZo6N2O5ui7Xv6Mue6jrcpG6LjdMVf3ZixZdWxN6+jpp6kv7P4RbJ67R0LkpM0/9SHpKGtsnZ/fnX26cyo6pvsTHvKbjsG64fumBgZkxUw9/z60Twez0KfqfDnlbJ322TkEik3KzgPqs5ezA5/+uIDMlnYA89Zl3XYBjPhuy/kxAZloGx3w2AHB981HcVoxi6LElIJFwc/dpXt55WtLdJmigFH/HTSOJsjw/OqWQHB0duXZNNZO0dOlSDh48SN++fRk3bhze3t40aNAgawbr9u3bNGjQgJCQEGbOnImvry9Dhgxhw4YNVK9endGjRzNs2DDatGnzzm1evHiR33//nXXrsg8+Li4u7NmzB1NTU/bu3UtoaCizZs3Ktczf358nT54wc+ZMLly4gJeXF8ePH6datWqMHj2aHj168OzZM168eJG1bkGSFnuVZLd9MD1+LtmMyYdyaHvJH5X+IbT7dH1ZRyjQ98rqZR1BoyGppfPwhtKWkple1hEKlF4K9799CMY6lco6QoH6GduXdQSN4pQle0Lhh2IrKd79b/8LRopSfMRvKQqWlN5TdEuTqeTDPmyoJGopyu+cw8Qn+e+vFYqnkl6tMt1+UnJYmW6/IP+ZSyXfGj58OK9fZ18+MGPGDEJDQ/Hw8MDd3T3rnrPvvvuORYsWYWFhwdSpU5k+fTpKpZLevXsze/bsYj2cpDA8PDwIDQ2lT58++Pv7Y2trC8D+/fvR0tLCw8ODUaNGcePGDc6fP1/q2xcEQRAEQRAE4f8//4kZt/+rxIxb0YgZt6ITM25FI2bcik7MuBWdmHErOjHjVjRixq14xIxb6dHVrVmm209JCS/T7RfkPzfjJgiCIAiCIAiC8F9Tfk9blANfffUVz57lfrz2pEmTcHJyKqNEgiAIgiAIgvDfJi4I1EwM3N7h559/LusIgiAIgiAIgiAI4lJJQRAEQRAEQRCE8k7MuAmCIAiCIAiCUG6Iv+OmmZhxEwRBEARBEARBKOfEjJsgCIIgCIIgCOWGeDiJZmLGTRAEQRAEQRAEoZwTAzdBEARBEARBEIRyTlwqKQiCIAiCIAhCuSEuldRMzLgJgkD/CmYAACAASURBVCAIgiAIgiCUc2LGTRAEQRAEQRCEckPMt2kmZtwEQRAEQRAEQRDKOYlSXEQqCIIgCIIgCIJQrokZN0EQBEEQBEEQhHJODNwEQRAEQRAEQRDKOTFwEwRBEARBEARBKOfEwE0QBEEQBEEQBKGcEwM3QRAEQRAEQRCEck4M3ARBEARBEARBEMo5MXATBEEQBEEQBEEo58TATRAEQRAEQRAEoZwTAzdBEARBEMqFZcuWFeo1QSiuoKAg/P392b9/f9ZPefHgwYN8r128eLEMkgjllRi4/R/2xx9/5PpdLpezZs2aMkqT2w8//EBmZmbW74mJiUybNq0ME6k8fvwYLy8vPv74YwDu3LnDL7/8UmZ5AgMDiYiIyPp9zZo19OzZk9GjR/P06dMyywUQEBDwzp/y6M8//yzrCDx8+JDz58+TlJSU6/XTp0+XUSKVkJAQQkJCANWXi40bN3Lq1KkyzaTJlClTyjrCe82cObOsI+TStWvXso6Q5dy5c/leK+u2/1Z5+8I/evTod/6UtfJ2vASYPHkyS5cu5erVq9y4cYMbN24QGhpapplymjBhAr6+viiVSlJTU5k/fz4rV64s61hCOaJV1gGEsnPhwgUCAgJYuHAhcXFxTJs2jVatWpV1LEA1iPz0009ZtGgRL1++ZP78+QwZMqSsYzFz5kymTJnCrFmzAKhfvz6TJk1i7NixZZJn1apV7N69G4ATJ07g7+/PihUruH37NnPmzGHDhg1lkuttnrcCAwNxcXHJtdzNze1/Hem9Vq9eTd++fcts+1u2bMHPzw87Ozvu3LnD9OnT6dy5M6Cq644dO5ZJrjVr1nD69GkyMzNp3749169fp1WrVvj6+nLr1i3GjBlTJrk0fTm9ePFi1uu//vrr/zpSlri4OI2vK5XKMh3wOjo6IpFIsrIApKamZr0eFBRUJrm2b9/Ojh07ePr0KR4eHlmvJyUl0axZszLJlNPkyZN5+vQp9evXRyaTASCRSOjVq1eZZQoODsbKyooePXrg4OCQVZ/lRXk7XgKEhoZy8ODBrM9AebN7926WL1/OgAEDSEpKwsPDgx07dpR1LKEcEQO3/8NWrFjBwYMH8fDwQFdXlxUrVtC8efOyjgWAt7c3bdu25dNPP8XQ0BA/Pz9q1qxZ1rFISUnB3t4+12tvD+JlQSKRoKurC6hmuPr27Uvjxo1p3Ljx/2vv3oOqLPc9gH9R5F5HUDJL0sQILxkqhh7MS6a4Qa4BYsdStLP3NtG8RpqgbVTMzc4y22ozxiVTS0WUhSOecgxlzAuXRLkYouJlJ4aYArKWsN7zB8MrS0DBcj2Pru9nphl4VzPrO6is9/c+v9/zYPPmzcJyAUBsbKz6dUBAgMH3IjW+Kbzbb7/9ZsQkTW3btg3JycmwtbXFxYsXMWvWLFy6dAmTJ08WelOWnp6OlJQU6HQ6eHp6IiMjA3Z2dnjnnXcQEhIirHC7cuUKnJ2dERISAjMzMyiKgpMnT2Lq1KlC8jQ2dOhQPPPMMwZ/bg0Zr127JixXUFAQbt68iffffx+dO3cGALz22mvYv3+/sExA/b/L4cOH45NPPsG8efPU67a2tujYsaPAZPVkvOHPzMxEZmYm0tLSoNFoMGLECIwfPx4vvPCC6GgA5Pu8BIAXXngBV69exVNPPSU0R0vMzc1haWmJmpoaaLVadOvWDe3asTmO7mDhZsLOnTuHpKQkeHl54cyZM9i1axf69OmjFgIiHTt2DMuXL8eMGTNw+vRp/OMf/8CKFSvQpUsXobns7e1RWlqqfnjv3bsXjo6OwvIoioKqqipYW1vjp59+wptvvqm+ptVqheW6m0w3O+Xl5di4cSOefPJJg+uKoiAsLExQqnp1dXWwtbUFAHTr1g1ff/01Zs2ahcuXLwst3Nq3b4/27dvD2toazz33HOzs7AAAVlZWQm8qduzYgaSkJKxfvx7vv/8+evfuDUtLSyk6B5ycnJCQkIBnnnmmyWsjRowQkKheVFQUTp48iblz5+L111/HpEmTpPj3+cQTT+CJJ55Q28LKy8uh1WpRXV2N6urqZn+OxiTjDX/79u0xfPhwDB8+HDqdDhqNBm+99RZmzJghRYeKbJ+XAFBRUQEfHx/0798fHTp0UK+LXJ1vLDg4GKNHj8b27dtx/fp1REdHIz09HWvWrBEdjSTBws2E/f3vf8eSJUswdOhQKIqC+Ph4BAcHIy0tTXQ0fPzxx/jss8/Qq1cvAPWrSZMnT8bevXuF5lqyZAmioqJQUlKCV199Fd26dRM6OD958mQEBATAzs4OPXv2xEsvvQQAyM/PF/4BKauRI0eiqqoKvXv3bvKah4eHgER3dO7cGQUFBWo2W1tbbNiwAYsWLcLp06eF5erQoQNu3boFa2trJCcnq9dv3rwptHBr164dpkyZgnHjxmHFihXo3Lkz6urqhOVpbPLkybhx40azBcc777wjINEd/fr1Q0JCAjZt2oRJkyZJ9ZBn//79WLlyJcrKyuDg4IDLly/D2dlZ+OeSrDf8Op0OBw4cgEajwaVLl/DWW29J04be3OdlXFyc0EwzZ84U+v73s3z5cvVz3NHREevWrRM+S0lyMVNka4omo6msrFSfnDc4d+4cevToISZQI3V1dU1aKioqKmBvby8okaHq6mro9fomPz8Rrly5gvLycri6uqo30WVlZaitrRX6lLrx/NHx48fh7u5u8LroGx4Z/frrrzA3N1db2BrLysoS1sqs0+lgYWHR5HpFRQXKysrw4osvCkjV1IEDB5CdnY25c+eKjoLc3Fy4ubmJjtHE3bnKyspQUFAgdBWwMT8/PyQmJiI8PBwpKSn46aefkJaWhpiYGKG5jh492ux1kau7kZGR+OWXX/Dqq6/Cx8cHLi4uwrLci0yfl0B9S3xeXh4AoH///ujUqZPgRE01rDg3EL3iTPJg4WbiTp8+jeLiYuh0OvWayGHrxg4cOIBffvnF4JdXRESEwERAYmIi3njjDdja2mLx4sXIz8/HvHnzMGzYMCF5pk+fjkGDBmHAgAF46aWXmr25FqWlG50Gom54Tpw4gYqKiiY3qj/88AO6dOmCfv36CckFtJxt//79eOqpp4Rlk/VnJmsuAAgMDMTOnTuFvX9LZM3VICgoCMnJyfDz80NKSgratWuH4OBgbN++XXQ06W74XV1d1dGGxu2uiqII3WimQUVFBb744gtkZWXBzMwMAwcOxIwZM4Q+gN2zZw/++c9/4pVXXoGiKDh+/Djef/99jBs3TlimxmRdcSZ5sFXShK1duxZHjhzBmTNnMGLECGRkZGDQoEFSFG7R0dGoqanBkSNHEBISgvT0dLV9QKQdO3Zg8uTJOHjwIMrLyxEbG4uFCxcKK9xCQ0ORk5ODTz/9FEVFRejZsycGDBiAgQMHYsCAAc2u3BjLvQqz2bNnCyvc/vnPfza7UUqvXr0QFRWFpKQkAanqtZTN2dlZaDZZf2ay5qIH9+STT6KqqgqDBw/G/Pnz4eDgAHNz8bcqd9/wx8TECL/hLywsFPberTF37ly4u7ur81mpqamYM2cOEhIShGVav349tm/frhbd165dU9utZfDpp5/i22+/bbLiTNRA/G9DEiY9PR27du1Sd/z77bffsHjxYtGxAAA5OTlITU2Fr68vIiIiEB4eLkVvesMC9Y8//og33ngDrq6uQjeNGDVqFEaNGgWgvr00Pz8fR48exapVq3Dx4kUUFBQIy3Yvubm5wt77+vXr6NatW5Pr3bt3b3ELd2ORNRtztd2FCxfueZaWqFZhWXM1+Pe//w1LS0ssXLgQqampuHnzJmbMmCE0EyDnDf/hw4cxdOhQAPV/rk5OTupr+/btEz7r9vvvvxv82b377rv4/vvvBSaq/wxvvFLasWNHqY5RMDc3h729PfR6PfR6PYYMGSJ8LpDkwsLNhFlaWqJdu3YwNzdHZWUlOnXqJPzQ5gZWVlYAAGtra1y5cgX29va4ePGi4FT1Q/1Tp07FxYsXMW/ePFRWVgrfqvfatWvIyclBTk4Ofv75Z2i1WgwdOhQDBgwQmktWNTU1Lb5269YtIyZpStZszNV2Dg4OUhxLcDdZczWwsbFRvw4MDBSYxJCMN/yrVq1S215nzZpl0AK7bt064YWbh4cH0tLS8Je//AVA/a6SI0eOFJpp2LBhmDZtGnx8fADUr6SKOh+zObKuOJM8+LfBhPXr1w83btxASEgIgoKCYGNj0+TMFVFGjhyJGzduYNq0aQgKCoKZmRmCg4NFx8Ly5ctRUFAAJycnWFtbo6KiAitWrBCWZ+zYsbCzs4OXlxeGDRuG6dOnq9vJi3bq1KlmryuKgtraWiOnuWPo0KFYvXo1Zs+ebTAXsmbNGgwZMkRYLkDebMzVdra2tlIcS3A3WXM1Phi8MVnmtWS84W9cON5dRIouKgFg69atuHXrFhYsWAAA0Ov1sLa2Rnx8vLA/08jISKSnpyM7OxuKomDChAkYM2aM0XO0RNYVZ5IHNychAMDFixdRWVkJV1dX0VGa0Ol00Gq1eOKJJ0RHAVDfgtIwbD1o0CChv/Q3bNiA3NxcXLlyBc8//zzc3Nzg5uaGPn36CD/o9H7nCH399ddGSmKouroaixcvxokTJ9Rt9wsLC9GvXz8sW7ZMaOErazbmaruIiAisXbtW2Pu3RNZcj4LGN/yDBw8WfsPfeKOZuzedkX0TGrq3yspKgwecMhxCT3Jg4WbiCgsLcenSJYOzj0S3VwD181oHDhxoki08PFxgKmDp0qUoLS01eOr63HPPYcmSJUJzAcDZs2eRk5OD3NxcHD9+HA4ODti0aZPoWPeVmZkJT09Po7/vhQsX8MsvvwCoP1y38XyIaLJmY67WO3v2LFatWoULFy7AxcUFkZGR6NKli+hY0uaitnN3d1ePWbn7yJWsrCwcO3ZMVDTVDz/8gOPHjwOo37CqYSbb2CZOnIgtW7Y0WdmVZUW3wdatW7FmzRpYWVnBzMxMzffDDz+IjkaSYOFmwhYuXIiioiK88MILBnNaze3SZmz/+7//C0tLS7i4uBhkE30cgI+PDzQajfqLX6/Xw9fXV/iuTxcuXEB2djays7ORk5ODsrIyvPzyy9iwYYPQXK1h7CfDBw8eRFVVVZNNBXbv3o1OnToJKSIbyJqNudruzTffREBAANzd3bF//37k5uZKsdIlay5ZydzCKeuRKw3i4uKQl5cHX19fAEBaWhr69u2L+fPnC80ls7Fjx2Lr1q1wcHAQHYVkpZDJ+stf/iI6QovGjx8vOkKzZsyYoVy8eFH9/uLFi8qcOXOE5Xn33XcVT09PxcvLS/nggw+U7777Tvnll1+E5XkQ/v7+Rn2/kJAQpby8vMn1srIyJTQ01KhZ7iZrNuZqOz8/P4PvAwICBCUxJGsu+mPKy8ub/bcg0vjx45W6ujr1+9raWuGf7fPnz2/VNVGmTp2qVFdXi45BEuPmJCbMzc0NxcXF6NWrl+goTQwfPhyHDh0Sdj5aS65fvw5vb291E5e8vDy4ubmp22sbeyvtoKAgxMTE3PfpnKh2xNZo7mn2w3Tr1q1mf16Ojo6orq42apa7yZqNudpOq9UiPz9f3SSipqbG4Pu+ffsyF/0hiqLgiy++UOeF9Xo92rdvj0mTJgnvTmlw48YNdT7r5s2bgtMAxcXFBt/X1ta2uJGWCPPmzUNYWBhefvllWFhYqNdlOaqJxGPhZsICAwMxYcIEdO7c2eAXRGpqqsBU9dzc3BAREQG9Xg9zc3Mp2lKA+i2XZTJ69OhW/X9xcXHSFm7GptPpUFtb22SL5du3b0Or1QpKVU/WbMzVdo6OjgZt5507d1a/NzMzE3Y4uKy5qO0SExORlZWF7du3q3OdFy5cwNKlS5GQkIApU6YIzfe3v/0NgYGB8PDwgKIoOHbsGObNmycky4YNG7B+/XpotVoMHDgQQH3ha2FhgdDQUCGZmhMdHY0hQ4Y0GRMhasAZNxM2ZswYfPDBB01+QTz77LMCU9UbPXo0vvjiC7z44otGX5F5HAUEBCAlJUV0jGYZe5e7uLg4lJeXIyoqSj0zqrq6GsuWLYO9vb26dbUIsmZjLiL5BAQE4Kuvvmqy6nzt2jVMnTpVit/5ZWVlyMvLg6IoePnll+Ho6Cg0z7/+9S9hxWNrhIWFYevWraJjkMS44mbCunbt2uoVG2Pr0aMHXFxcpCvacnNzERMTg5KSEty+fRt1dXWwtrYWvhJ4PyJ+jidOnEDXrl3VD+qUlBSkp6fj2WefRUREhNo+Y+yNEWbPno1PP/0Uo0aNUh9SXL58GcHBwXjvvfeMmuVRycZcbXfu3DmsWrUKpaWlUu3eKGsuarva2tpmW4UdHByEnpV5d+vh008/DaC+iCsrKxPajjtv3jz8/vvvOH/+vMGq/ODBg4VlaszDwwPffvstRo0aZdAJxeMAqAFX3EzY0qVLcfPmzSa/IGQ4DuCDDz7AhQsXMHz4cINsoo8DCAoKwurVq/Hee+9hx44dSElJwfnz5zF37lyhue5HxJk+gYGBiI+PR8eOHXHs2DHMmTMHUVFRKCgoQElJCdasWWPUPHerqanB+fPnAQDdu3eHlZWVwesi5wJlzcZcrSfr7o2y5qK2u9fvdZHnuL311lvqVvZA0weHIttxt23bhqSkJPz6669wdXXFzz//DDc3N2lahF977bUm13gcADXGFTcTptVqYWFhgczMTIPrMhRu3bp1Q7du3XD79m3cvn1bdBwD3bt3R11dHdq3b4833ngDYWFhoiPdl4j217q6OvUp4Z49ezBhwgR4eXnBy8sL/v7+Rs9zNysrK7z44ostvi5yLlDWbMzVelVVVersTM+ePREYGGjU92+JrLmo7QoLC9V5rcYURYFOpxOQqN6CBQvw9NNP46mnngIA7Ny5E+np6ejWrZvwTVOSkpKwfft2hIaG4uuvv8aZM2fw+eefC80E1HfzuLm5Yf/+/aKjkORYuJmgTZs2YdKkSVKc19YS0b/cW2JtbQ2dTofevXtj1apVeOqpp4TuXidrOyJQv8NZw8YRhw8fRkxMjPpa40PVZSVzM4Ks2ZjrDll3b5Q1F7VdQUGB6AjNWrJkCeLj4wEAx44dw7/+9S+12yI6Olpot4WFhQUsLS0B1G9u5OzsjLNnzwrL0+Cjjz4StkJKjxYWbiZox44dmDRpkugYzWrYVr8lxt5u/26rVq2CXq9HdHQ0EhIS8J///Efo07q7PyDj4uKk+YD08fHBpEmTYG9vDysrK7i7uwMAzp8/Dzs7O2G5Wku2+crGZM3GXHfIunujrLno8SFzt8XTTz+NGzdu4PXXX0d4eDiefPJJdWWQ6FHAwo2kMnXqVAD1T8ijoqKwbNkywYkMNW45lGFVUOYPyOnTp2Po0KG4evUqPD091ZtnvV6PqKgoodmIHraGs7Xux9jzd7LmoseHzN0WX3zxBQBg5syZ8PDwwM2bN/Hqq68KzQTUH+NwrwfXoh9akzxYuJmgoqKiFvviRZ+V9sorr6hf29jYGHwvkq+v7z1fF3X2ncwfkFqtFrm5uSgtLUV5eTmCg4Nhbm6O559/Xmiu1pLhWIyWyJqNudpO1jMWZc1F8pOx26KyshJ2dna4fv26es3FxQVA/REijTdBE8HBwUF9cE10LyzcTJCLi4sU57vcj0xtV7I+7ZLxA7JBZGQkzM3N4e7ujoyMDBQXF2Px4sVCMwFyzwXKmo25Hh7OBdLjRsZui3nz5mHDhg0ICgoy2PESkGPXRltbW2keVJPkFDI5/v7+oiO0qKKiQv3P19dXuX79usE12YWGhhr9PXNycpR9+/YpVVVV6rWSkhLl5MmTRs/S2Pjx49Wvb9++rQQEBAhMc0dAQID6d+no0aOKp6ensnfvXmX16tXKzJkzmY25jEqWfxd3kzUX0eNoxowZrfr/Dh069JCTkOy44maCxo0b16r/b8OGDfjb3/72kNMYuvtpWOOtqmV4KnY/jQ/0NNb7ydqOaG5u3uzXosk8FyhrNuYiokfZ3YeC3030bqqt7QpgCzPJczdFRnO/nRsb7N271+iF26N+homx2ztlbUcEDM8YUhQFWq0WAwcOFD5LKfNcoKzZmOvhkXX+TtZcRA9i5cqVAOqPADh58qR67mNRURH69++PLVu2iIzXagpbmE0eCzdqkUy/IEpKSvDVV19Jt8ukaGfOnFE3RgkODkZISIjgRHfIesaQzHOBsmZjrraTdf5O1lxED1PDbqpz5szBP/7xD7VwO336NL766iuR0dpEptl/EqOd6AAkLxG/IAoLCzF16lSMHz8eq1evxm+//YaZM2diypQp6NWrl9HztJWxi11Z2xFbUl1djd27d+Ovf/2rsAzTp0/HBx98gKCgIGzevFmKwXnZszFX2y1ZsgQdOnQAcOeMxYCAANjZ2SE6Opq5iAQoKSlRizagfrM2WR8yEjVH/js9EkbEiltUVBQmTpwINzc3HDx4EIGBgRg/fjzi4uJgaWlp9Dx3Ky4ublJAHjlyBB4eHgDqD+g2JlnbERvT6XTIyMhAamoqDh48CC8vL4SFhQnLI/NcoKzZmKvtZJ2/kzUXkTE4Ozvjww8/hJ+fH8zMzLB79244OzuLjqXS6XRNjiZofI0tzMQVNxOWlZV1z2ut3cTkz6TT6RAUFISePXti8uTJaNeuHebPny9F0QYAs2fPxpdffglFUVBTU4OYmBh88skn6usN58IYS0FBAbKzs5GdnY2cnBzk5+erX4su2jIzM7Fw4UKMHj0ae/fuhb+/Pzp27IjY2Fi89tprwnJFRkbi5MmTcHFxQUZGhjr7IANZszFX2zXM3wHA4cOHMWTIEPU1GeYCAblyERlDbGwsXnjhBSQlJSExMRG9evVCbGys6FiqCRMm3PMaW5iJK24mbNmyZdi5c2eL11q7icmfSavVIj8/X13ts7GxQVFRkfq96J2fvvvuO8TFxSEsLAxVVVXw9fWVbqi5uroa33//PTQaDb788kthOaZNmwZ3d3ds3rwZTk5OAIDly5cLy9NA5rlAWbMxV9vJOn8nay4iY7C0tMSUKVMwZcoU0VEMXL16FVeuXEFNTY3BPVBlZSVu3bolOB3JhIWbCcrJyUFOTg6uXbuG+Ph49XplZaXwJ66Ojo4GT786d+6sfm9mZoakpCRR0QDUz5FZWlqipqYGWq0W3bp1Q7t24heuZWtHBIDk5GSkpaUhPDwcTk5O8Pb2hl6vF5oJkHsuUNZszNV2Mh5CLHMuImM4d+4cPvnkExQXFxsc3yP6qKFDhw4hOTkZv/76K1auXKkWbnZ2dpg7d67QbCQXM0WmrQPJKI4ePYqjR49i69atBjf3tra2GDVqFHr06CEuXCtlZmYKOcvEz88Po0ePxrvvvovr168jOjoaHTp0wJo1a4yeBaj/OWg0Ghw6dAgeHh7w9vbGsmXLpDtWISsrC2lpadi3bx9cXV0xZsyYZltCjKF3796wtrYGcGcu0MrKSoq5QFmzMVfbabVabNmyBaWlpXBxcVHn70STNReRMUycOBGzZs3CihUrsH79eiQnJ0NRFMyaNUt0NABAeno6vLy8RMcgibFwM2GXLl16ZAddAwMDm7R5GkNeXh5eeuklg2spKSkICAgwehYAcHV1hbu7O2JjY9V2xNGjRwt/etgSvV6PzMxM7NmzR6q5AqI/2+zZsw3OWHzmmWekOGNR1lxExhAUFITk5GT4+vqqbdZvvvkmNm/eLDhZvQULFiA6OhpPPPEEgPr7tEWLFiExMVFwMpIFH7OZMJ1Oh6ioKFy6dEkdVgcgvB2xNUQ9b2go2srLy9U2i1deeUVIFkDedsQGFRUV0Gg0KCkpAVC/o5ePjw9effVVwckMyTIX2BxZszHXvck6fydrLiJjsLCwgF6vR/fu3bFp0yZ06dIF5eXlomOpBg0ahJCQECxcuBBXrlzBxo0bERkZKToWSYSFmwl77733EBYWhpCQECnmtNpC1CGU+/fvx8qVK1FWVgYHBwdcvnwZzs7OSEtLE5KnT58+6NOnDxYsWKC2I96+fRvvvPOO0HZEoP4GcfLkyRg2bBh69+4NRVGQl5eH9evXIykpCT179hSWDZBzLrCBrNmYq/Vknb+TNReRMSxatAi3bt3C4sWL8dlnn+HIkSP4+OOPRcdShYWFoVevXpg8eTLs7e2xc+dOODo6io5FEmGrpAlraBl4FIlqlfTz80NiYiLCw8ORkpKCn376CWlpaYiJiTF6lpbI0o44a9YsjBs3Dt7e3gbX09PTodFo8PnnnwvJJfNcoKzZmKvtZJ2/kzUX0cNWV1eHuLg4qVewUlJSsG7dOsycORNFRUU4dOgQYmNj4erqKjoaSYKP20zYqFGj8M0332DMmDEGBz42HM4qM1Gzeebm5rC3t4der4der8eQIUMQFxcnJEsDWdsRi4qKmt20xcvLy+DsO2OT9ZgCQN5szNV2BQUFoiM0S9ZcRA9b+/btcerUKfUhhYz27duHzZs3o1OnThg/fjzGjBmDyMhI7Nq1S3Q0kgQLNxPWsGK1ceNG9ZqZmZnQjS1OnDiBrl27qq0BKSkpSE9Px7PPPouIiAi1qBR1COWTTz6JqqoqDB48GPPnz4eDg4PQdiOZ2xFtbGwe6LWHTea5QFmzMdefQ5b5u7vJmovoz9anTx9Mnz4d48aNM/gcGjt2rMBUd/z73/8GUP9v0sbGBv3798e2bdsEpyKZsFWSpBIYGIj4+Hh07NgRx44dw5w5cxAVFYWCggKUlJQI23a/QXV1NSwtLaEoClJTU3Hz5k34+vrC3t5eSB5Z2xEBYPjw4QgPD29yXVEUJCYm4scffxSQypBMxxTcTdZszNU2zc3fjRkzBq+99hpzERnZwoULm70uyy7HOTk5+PDDD1FdXY0DBw6gsLAQW7duxdKlS0VHI0mwcDNht27dQnx8PP7zn/8gJiYG586dw9mzZzFq1ChhBfKtDgAADUdJREFUmfz8/LB7924AwEcffQQHBwfMnDkTAODv7y9Nu0BlZaXBTpyi2ku9vLyQnp7e5teM4X6rohEREUZKcn+yzAU2R9ZszHVvss7fyZqLiICQkBCsWbMG06dPR0pKCgBg/Pjx0Gg0gpORLNgqacIWLlyIvn37IicnBwDw9NNP47333hNauOn1etTW1sLc3ByHDx822PSjrq5OWK4GW7duxZo1a2BlZQUzMzO1V15Ue6ms7YjAvQuzhIQE4wVphqxzgYC82ZirbWSdv5M1FxHV69q1q8H3j9qu3/RwsXAzYaWlpfj000/VrewbdhYTycfHB5MmTYK9vT2srKzg7u4OADh//jzs7OyEZgOAr776ChqNBg4ODqKjAKg/Ty4+Pr7JdUVRcO3aNQGJWichIQFTpkwR8t4yzwXKmo252k7W+TtZcxFRfdGWnZ0NMzMz6HQ6fP3113B2dhYdiyTCVkkTFhYWhoSEBEycOBE7d+5EaWkp5s6di+3btwvNlZubi6tXr8LT01NdNTp79iyqq6vRt29fodmmTZuGtWvXqttpi/YotSM2NmLECGEzbjLPBcqajbn+GFnn72TNRfQw6PV67N27t8nvC5lcu3YNy5cvx+HDh6EoCjw9PfHhhx8Km6Mn+bBwM2GZmZlYt24diouL4enpiZycHMTGxsLDw0NYJq1Wiy1btqC0tBQuLi4IDg6W6pDY/Px8LFy4EC+//LLBEQqLFy8WmKp5Ile17mfkyJE4cOCAkPeWeS5Q1mzM9eeQZf7ubrLmIvqz/c///A+++eYb0TGIHpg8d8RkdJ6enujTpw9+/vlnKIqCDz/8UHgLYGRkJMzNzeHu7o6MjAwUFxdLVRRFR0djyJAhcHFxkb7vXHThNmDAgGbPymk49FcUmecCZc3GXA9G1vk7WXMRPWz//d//jY0bN8Lb29ugc0b0+bX36p4xMzPDjBkzjJiGZMbCzYRlZWWhd+/eGDlyJHbt2oUNGzbg7bffFna4NVA/s5KamgoACA4ORkhIiLAszTE3N29xO2HZiF5Mb9j0RjYyzwXKmo252k7W+TtZcxEZw44dOwDAYNVN9Pm1QPMPmqqrq7Fjxw5cv36dhRupWLiZsKVLl2L37t0oLCzExo0b8cYbbyAyMhKbNm0SlqlxW6RMLZINPDw88O2332LUqFEGrZKin9Y1p7nVLgJCQ0NRVVXV7GuiHxTImo252u6zzz7DokWLmp2/W716tbD5O1lzERmDrMdeTJ06Vf26srISSUlJSE5Ohre3t8FrRJxxM2GBgYHYuXMn1q5diy5duiAkJES9Jkrv3r3V9oWGlrqG3S7NzMyQnZ0tLBuAZg+nFfm07n7tiPn5+QJSPbpEt5fei6zZmKt5ss7fyZqLyFhOnz6N4uJi6HQ69VpAQIDARPWuX7+O+Ph4pKamIjAwEG+//Tb+67/+S3Qskox8SxpkNLa2ttiwYQN2796Nb775BnV1dQaHSotQUFAg9P1bkpubCzc3N+me1snajvioEn2zfy+yZmOu5sk6fydrLiJjWLt2LY4cOYIzZ85gxIgRyMjIwKBBg4QXbh9//DH+7//+D6GhoUhNTYWtra3QPCQvFm4mbPXq1dBoNFixYgUcHR1x+fJlTJs2TXSsJqqrq/H9999Do9Hgyy+/FJLho48+EroSScYhcwOCrNmYq3myzt/JmovIGNLT07Fr1y4EBAQgNjYWv/32mxQboMXHx8PCwgLr1q3D+vXr1euydBuRPFi4mTBHR0eMHTsW58+fBwDY29vj9ddfF5yqnk6nQ0ZGBlJTU3Hw4EF4eXkhLCxMdCx6zMk8FyhrNuZqnqzzd7LmIjIGS0tLtGvXDubm5qisrESnTp1w4cIF0bFQWFgoOgI9Ili4mbDvvvsO3377LX7//Xd8//33uHLlCpYsWYLExERhmTIzM6HRaHDo0CF4eHjA398feXl5ws8WunDhAv7+97+3+HrjJ2QkN1mPKQDkzcZcbRcREdHiawkJCcYLchdZcxEZQ79+/XDjxg2EhIQgKCgINjY26N+/v+hYRK3GzUlMmL+/P7Zt24bQ0FCkpKQAAHx9fdXt+EVwdXWFu7s7YmNj4eTkBAAYPXq08K16x44di2XLlrX4+iuvvGLENET0KBN5AP29yJqL6GG4ePEiKisr4erqKjoKUatxxc2EWVhYGGxpL3pjEgBITk5GWloawsPD4eTkBG9vb+j1etGxYGtry+KMiP4Usj4vlTUX0R916tSpe77Wt29fI6YhenAs3EzY4MGDsX79etTU1CAzMxObN29udrt7Y+rTpw/69OmDBQsWICsrC2lpabh9+zbeeecdjBkzBhMmTBCSq7WHkmdmZsLT0/MhpyGiR5no+buWyJqL6I9auXJli6+ZmZkhKSnJiGmIHhxbJU2YoijYtm0bDh06BAAYNmwYQkJCpPvw1uv1yMzMxJ49e4TPut2P6HPwiEgOsp6xKGsuIiK6PxZuJkqv18PPzw8ajUZ0lCYqKiqg0WhQUlICAHB2doaPjw/s7e0FJ7u/gIAAdV6QiIiI5HH79m1s2bIFx48fB1A/nz5hwgR06NBBcDKi1mknOgCJ0a5dO7z44ou4fPmy6CgGzpw5A19fX5w6dQo9evRA9+7dkZeXB19fX7WQk5lsq5VERERUb+nSpTh16hQmTpyIiRMn4tSpU1i6dKnoWEStxhk3E3b16lX4+Pigf//+sLa2Vq+L3Nr+s88+w6JFi+Dt7W1wPT09HatXr8bnn38uKBkRERE9yvLy8rB79271+6FDh8LPz09gIqK2YeFmwu51no8oRUVFWLNmTZPrXl5e+OSTTwQkapvWbmJCRERExtW+fXuUlpbiueeeA1B/Rmv79u0FpyJqPRZuJkir1WLLli0oLS2Fi4sLgoODYW4ux18FGxubB3rtYTtx4gS6du0KR0dHAEBKSgrS09Px7LPPIiIiAh07dgQArF27VlhGIiIiatn777+Pt99+G05OTlAUBZcvX8aKFStExyJqNW5OYoJmz54Nc3NzuLu7IyMjA8888wwWL14sOhYAYPjw4QgPD29yXVEUJCYm4scffxSQqn63yPj4eHTs2BHHjh3DnDlzEBUVhYKCApSUlDS7SkhERERy0el06sx8z549Dc6zJZKdHMssZFRnzpxBamoqACA4OBghISGCE90RGhqKqqqqZl8TmbOurk5dVduzZw8mTJgALy8veHl5wd/fX1guIiIiurfGXTMWFhYoLCxstmuGSHYs3ExQ47ZIWVokG9xr7i4hIcF4Qe6i1+tRW1sLc3NzHD58GDExMeprdXV1wnIRERHRvS1ZsgTx8fEAgGPHjiEuLk7tmomOjmbXDD0y5LprJ6MoLCzEwIEDAdw5dHXgwIFQFAVmZmbIzs4WnLB5CQkJmDJlipD39vHxwaRJk2Bvbw8rKyu4u7sDAM6fPw87OzshmYiIiOj+2DVDjwsWbiaooKBAdIQHInIcc/r06Rg6dCiuXr0KT09P9bw2vV6PqKgoYbmIiIjo3tg1Q48LFm70yBB5uLVWq0Vubi5KS0tRXl6u7sT5/PPPC8tERERE98euGXpccFdJksqAAQOaLdAaWjrz8/MFpJJ7J04iIiK6t9zcXLVrpuF4obNnz6K6uhp9+/YVnI6odVi4EbWCr6+vuhNnbW0tQkJCsHPnTsGpiIiIiMhUtBMdgOhRIPNOnERERET0+OOKG1Er9O7dG9bW1gDutG1aWVlJvxMnERERET0eWLgRERERERFJjq2SRA+ouroau3fvxl//+lfRUYiIiIjoMcdhHaI20Ol0yMjIQGpqKg4ePAgvLy+EhYWJjkVEREREjzkWbkStkJmZCY1Gg0OHDsHDwwP+/v7Iy8tDbGys6GhEREREZAI440bUCq6urnB3d0dsbCycnJwAAKNHj8YPP/wgOBkRERERmQKuuBG1QnJyMtLS0hAeHg4nJyd4e3tDr9eLjkVEREREJoIrbkRtlJWVhbS0NOzbtw+urq4YM2YMJkyYIDoWERERET3GWLgRPSC9Xo/MzEzs2bOHs25ERERE9FCxcCNqpYqKCmg0GpSUlAAAnJ2d4ePjA3t7e8HJiIiIiOhxx3PciFrhzJkz8PX1xalTp9CjRw90794deXl58PX1VQs5IiIiIqKHhStuRK0wa9YsjBs3Dt7e3gbX09PTodFo8PnnnwtKRkRERESmgCtuRK1QVFTUpGgDAC8vL5w+fVpAIiIiIiIyJSzciFrBxsbmgV4jIiIiIvoz8Bw3olYoLy9HfHx8k+uKouDatWsCEhERERGRKWHhRtQKoaGhqKqqava1kJAQI6chIiIiIlPDzUmI/qCEhARMmTJFdAwiIiIieoxxxo3oD0pISBAdgYiIiIgecyzciP4gLloTERER0cPGwo3oDzIzMxMdgYiIiIgec9ychKgVBgwY0GyBpigKtFqtgEREREREZEq4OQkREREREZHk2CpJREREREQkORZuREREREREkmPhRkREREREJDkWbkRERERERJJj4UZERERERCS5/wdBF1xPBhjFLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = Scaled_Data.corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    f, ax = plt.subplots(figsize=(16, 12))\n",
    "    ax = sns.heatmap(corr, mask=mask, vmax=1, square=True, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25e0852da90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qkvTTEXHK9tsaHrFL0o9KulJSQ/nYYI51oY4xR45klEfV35S0ZsThlyStO7tje3n59WZJKyXdJOlB20vKlxyXNH8Sy7xK0rvlEG+VdMOIcz2S/kjSNkmbJ7EG1BmCHKnZouGvmj3rfkmF8geJhyW12W6U9DVJX4qIYxqeI3/Kw/MvT0vqnsiHnRXaVq6nqOHR+RuSVP7g9XREPCPpUUmfs337JPSPOsQt+gCQOEbkAJA4PuxE3bK9U9KS8w5viIhdtagHmCimVgAgcUytAEDiCHIASBxBDgCJI8gBIHEEOQAk7v8BhxB106Qhi3UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8584549044597782"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "-2.778314602481859"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n",
    "\n",
    "# 计算下四分位数和上四分位\n",
    "Q1 = Scaled_Data['Next_Tmax'].quantile(q = 0.25)\n",
    "Q3 = Scaled_Data['Next_Tmax'].quantile(q = 0.75)\n",
    "\n",
    "# 基于1.5倍的四分位差计算上下须对应的值\n",
    "low_whisker = Q1 - 1.5*(Q3 - Q1)\n",
    "up_whisker = Q3 + 1.5*(Q3 - Q1)\n",
    "\n",
    "up_whisker\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>1.146897</td>\n",
       "      <td>-2.217884</td>\n",
       "      <td>-1.247235</td>\n",
       "      <td>-0.404519</td>\n",
       "      <td>1.025171</td>\n",
       "      <td>1.294520</td>\n",
       "      <td>0.671912</td>\n",
       "      <td>0.915515</td>\n",
       "      <td>-0.086042</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.295389</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2750</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.722336</td>\n",
       "      <td>-2.347124</td>\n",
       "      <td>0.720893</td>\n",
       "      <td>0.431804</td>\n",
       "      <td>0.990499</td>\n",
       "      <td>1.166849</td>\n",
       "      <td>1.394751</td>\n",
       "      <td>2.292510</td>\n",
       "      <td>-0.188155</td>\n",
       "      <td>3.329386</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.592600</td>\n",
       "      <td>-3.002504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>-1.443736</td>\n",
       "      <td>1.297482</td>\n",
       "      <td>-2.698896</td>\n",
       "      <td>3.154805</td>\n",
       "      <td>0.682660</td>\n",
       "      <td>1.256502</td>\n",
       "      <td>1.666250</td>\n",
       "      <td>1.293021</td>\n",
       "      <td>0.393944</td>\n",
       "      <td>-0.067187</td>\n",
       "      <td>0.123279</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.165416</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5026</th>\n",
       "      <td>-0.564278</td>\n",
       "      <td>1.141629</td>\n",
       "      <td>-2.189783</td>\n",
       "      <td>0.265648</td>\n",
       "      <td>-0.153150</td>\n",
       "      <td>1.590055</td>\n",
       "      <td>1.613945</td>\n",
       "      <td>1.371150</td>\n",
       "      <td>0.589398</td>\n",
       "      <td>-0.085760</td>\n",
       "      <td>0.215901</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>0.865895</td>\n",
       "      <td>-2.810342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5050</th>\n",
       "      <td>-3.067350</td>\n",
       "      <td>1.585246</td>\n",
       "      <td>-2.476358</td>\n",
       "      <td>1.396136</td>\n",
       "      <td>-0.782847</td>\n",
       "      <td>0.501491</td>\n",
       "      <td>-0.052399</td>\n",
       "      <td>-0.043444</td>\n",
       "      <td>0.101308</td>\n",
       "      <td>0.072443</td>\n",
       "      <td>-0.138037</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.131952</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6100</th>\n",
       "      <td>-1.646687</td>\n",
       "      <td>0.457765</td>\n",
       "      <td>-2.310616</td>\n",
       "      <td>1.833989</td>\n",
       "      <td>1.489480</td>\n",
       "      <td>1.192236</td>\n",
       "      <td>0.211053</td>\n",
       "      <td>0.341799</td>\n",
       "      <td>1.351480</td>\n",
       "      <td>-0.204227</td>\n",
       "      <td>0.056343</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.517310</td>\n",
       "      <td>-3.535766</td>\n",
       "      <td>4.003889</td>\n",
       "      <td>1.463660</td>\n",
       "      <td>0.894764</td>\n",
       "      <td>1.277177</td>\n",
       "      <td>2.148085</td>\n",
       "      <td>1.600057</td>\n",
       "      <td>0.084708</td>\n",
       "      <td>2.187907</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>-2.138584</td>\n",
       "      <td>-3.258721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.293287</td>\n",
       "      <td>-3.323528</td>\n",
       "      <td>3.380546</td>\n",
       "      <td>0.732305</td>\n",
       "      <td>1.483952</td>\n",
       "      <td>1.747604</td>\n",
       "      <td>2.166054</td>\n",
       "      <td>1.581048</td>\n",
       "      <td>-0.091321</td>\n",
       "      <td>1.378723</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>-2.150110</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6178</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>1.381461</td>\n",
       "      <td>-3.854778</td>\n",
       "      <td>5.508391</td>\n",
       "      <td>1.053051</td>\n",
       "      <td>1.129946</td>\n",
       "      <td>2.138966</td>\n",
       "      <td>2.557734</td>\n",
       "      <td>1.943433</td>\n",
       "      <td>0.196184</td>\n",
       "      <td>1.718829</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>-2.174068</td>\n",
       "      <td>-3.098586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6179</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.540625</td>\n",
       "      <td>-3.180434</td>\n",
       "      <td>2.709573</td>\n",
       "      <td>0.667897</td>\n",
       "      <td>1.427806</td>\n",
       "      <td>2.207221</td>\n",
       "      <td>2.466783</td>\n",
       "      <td>1.705215</td>\n",
       "      <td>-0.208367</td>\n",
       "      <td>1.190854</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>-2.181193</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6180</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.291101</td>\n",
       "      <td>-3.008869</td>\n",
       "      <td>3.443116</td>\n",
       "      <td>0.875766</td>\n",
       "      <td>1.527562</td>\n",
       "      <td>2.186085</td>\n",
       "      <td>2.380435</td>\n",
       "      <td>1.732806</td>\n",
       "      <td>-0.111499</td>\n",
       "      <td>0.530213</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-2.150112</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.726924</td>\n",
       "      <td>-3.416935</td>\n",
       "      <td>3.153251</td>\n",
       "      <td>1.033799</td>\n",
       "      <td>1.484045</td>\n",
       "      <td>2.193447</td>\n",
       "      <td>2.587556</td>\n",
       "      <td>2.013460</td>\n",
       "      <td>-0.123314</td>\n",
       "      <td>0.618552</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-1.931225</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845437</td>\n",
       "      <td>-2.193176</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6182</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.128079</td>\n",
       "      <td>-2.987955</td>\n",
       "      <td>3.182614</td>\n",
       "      <td>0.539878</td>\n",
       "      <td>1.737777</td>\n",
       "      <td>2.324832</td>\n",
       "      <td>2.536665</td>\n",
       "      <td>1.386484</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>0.452257</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>-1.024766</td>\n",
       "      <td>-0.172266</td>\n",
       "      <td>0.223191</td>\n",
       "      <td>-2.197299</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.559954</td>\n",
       "      <td>-3.090460</td>\n",
       "      <td>3.785644</td>\n",
       "      <td>0.588896</td>\n",
       "      <td>1.794153</td>\n",
       "      <td>2.241939</td>\n",
       "      <td>2.621514</td>\n",
       "      <td>1.616357</td>\n",
       "      <td>0.058270</td>\n",
       "      <td>0.359513</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.201502</td>\n",
       "      <td>-0.616299</td>\n",
       "      <td>-2.121602</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>-2.390844</td>\n",
       "      <td>0.548491</td>\n",
       "      <td>-3.356360</td>\n",
       "      <td>3.778144</td>\n",
       "      <td>0.340973</td>\n",
       "      <td>1.722154</td>\n",
       "      <td>2.379023</td>\n",
       "      <td>2.612524</td>\n",
       "      <td>1.583916</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>0.585596</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-0.458230</td>\n",
       "      <td>2.701715</td>\n",
       "      <td>2.861413</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6185</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.073370</td>\n",
       "      <td>-2.977437</td>\n",
       "      <td>2.994312</td>\n",
       "      <td>0.760935</td>\n",
       "      <td>1.561845</td>\n",
       "      <td>2.072250</td>\n",
       "      <td>2.185478</td>\n",
       "      <td>1.552148</td>\n",
       "      <td>-0.177150</td>\n",
       "      <td>0.826247</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>1.178432</td>\n",
       "      <td>-0.611095</td>\n",
       "      <td>-0.462470</td>\n",
       "      <td>-2.131853</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>-2.357019</td>\n",
       "      <td>0.397078</td>\n",
       "      <td>-3.309947</td>\n",
       "      <td>3.641401</td>\n",
       "      <td>0.469220</td>\n",
       "      <td>1.452282</td>\n",
       "      <td>2.096269</td>\n",
       "      <td>2.496450</td>\n",
       "      <td>1.974161</td>\n",
       "      <td>-0.132036</td>\n",
       "      <td>0.559798</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>-2.046890</td>\n",
       "      <td>-3.739128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6187</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.203483</td>\n",
       "      <td>-3.346815</td>\n",
       "      <td>2.572588</td>\n",
       "      <td>0.430980</td>\n",
       "      <td>1.597681</td>\n",
       "      <td>1.913452</td>\n",
       "      <td>2.183086</td>\n",
       "      <td>1.684987</td>\n",
       "      <td>-0.163895</td>\n",
       "      <td>1.556727</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>1.153252</td>\n",
       "      <td>-0.037504</td>\n",
       "      <td>1.043125</td>\n",
       "      <td>-2.208229</td>\n",
       "      <td>-3.130613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6188</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>0.196464</td>\n",
       "      <td>-2.927473</td>\n",
       "      <td>3.375313</td>\n",
       "      <td>0.684771</td>\n",
       "      <td>1.560831</td>\n",
       "      <td>2.231691</td>\n",
       "      <td>2.590861</td>\n",
       "      <td>1.712657</td>\n",
       "      <td>-0.069786</td>\n",
       "      <td>0.474994</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-0.810742</td>\n",
       "      <td>-0.569309</td>\n",
       "      <td>-0.466338</td>\n",
       "      <td>-2.181012</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6189</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.335249</td>\n",
       "      <td>-3.121586</td>\n",
       "      <td>3.246297</td>\n",
       "      <td>0.445623</td>\n",
       "      <td>1.492428</td>\n",
       "      <td>2.204697</td>\n",
       "      <td>2.533502</td>\n",
       "      <td>1.996110</td>\n",
       "      <td>-0.150345</td>\n",
       "      <td>0.469066</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.684844</td>\n",
       "      <td>-0.586289</td>\n",
       "      <td>-0.293244</td>\n",
       "      <td>-2.122811</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6190</th>\n",
       "      <td>-2.052591</td>\n",
       "      <td>0.342238</td>\n",
       "      <td>-3.133392</td>\n",
       "      <td>3.608837</td>\n",
       "      <td>0.689004</td>\n",
       "      <td>1.649470</td>\n",
       "      <td>2.317912</td>\n",
       "      <td>2.603041</td>\n",
       "      <td>1.430013</td>\n",
       "      <td>-0.014496</td>\n",
       "      <td>0.546835</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.376283</td>\n",
       "      <td>0.730359</td>\n",
       "      <td>-2.274529</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6191</th>\n",
       "      <td>-2.018766</td>\n",
       "      <td>0.443371</td>\n",
       "      <td>-3.559795</td>\n",
       "      <td>2.056200</td>\n",
       "      <td>0.187604</td>\n",
       "      <td>1.580071</td>\n",
       "      <td>2.165352</td>\n",
       "      <td>2.341953</td>\n",
       "      <td>2.004631</td>\n",
       "      <td>-0.191814</td>\n",
       "      <td>2.098272</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>1.354688</td>\n",
       "      <td>-0.154704</td>\n",
       "      <td>-0.408689</td>\n",
       "      <td>-2.146457</td>\n",
       "      <td>-3.386829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6193</th>\n",
       "      <td>-1.781989</td>\n",
       "      <td>0.683279</td>\n",
       "      <td>-3.546341</td>\n",
       "      <td>3.042051</td>\n",
       "      <td>0.351990</td>\n",
       "      <td>1.475464</td>\n",
       "      <td>2.204002</td>\n",
       "      <td>2.544886</td>\n",
       "      <td>2.064549</td>\n",
       "      <td>-0.164838</td>\n",
       "      <td>0.821398</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>-2.064552</td>\n",
       "      <td>-3.643046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6194</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>1.544363</td>\n",
       "      <td>-4.016045</td>\n",
       "      <td>5.989302</td>\n",
       "      <td>-0.963308</td>\n",
       "      <td>1.394206</td>\n",
       "      <td>2.330022</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.371793</td>\n",
       "      <td>0.122695</td>\n",
       "      <td>1.598140</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>0.158666</td>\n",
       "      <td>1.560277</td>\n",
       "      <td>2.534052</td>\n",
       "      <td>-1.925129</td>\n",
       "      <td>-3.450884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6195</th>\n",
       "      <td>-1.984940</td>\n",
       "      <td>0.181415</td>\n",
       "      <td>-3.033073</td>\n",
       "      <td>3.594376</td>\n",
       "      <td>0.729578</td>\n",
       "      <td>1.494870</td>\n",
       "      <td>1.970933</td>\n",
       "      <td>2.330075</td>\n",
       "      <td>1.676683</td>\n",
       "      <td>-0.125777</td>\n",
       "      <td>0.724950</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>0.611895</td>\n",
       "      <td>-0.655350</td>\n",
       "      <td>-0.499833</td>\n",
       "      <td>-2.156557</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6196</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.273083</td>\n",
       "      <td>-2.951532</td>\n",
       "      <td>3.104686</td>\n",
       "      <td>0.981366</td>\n",
       "      <td>1.602459</td>\n",
       "      <td>2.125604</td>\n",
       "      <td>2.416907</td>\n",
       "      <td>1.457133</td>\n",
       "      <td>-0.151949</td>\n",
       "      <td>0.611140</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.190000</td>\n",
       "      <td>-2.842369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6197</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.353661</td>\n",
       "      <td>-3.003557</td>\n",
       "      <td>3.407620</td>\n",
       "      <td>0.675525</td>\n",
       "      <td>1.534201</td>\n",
       "      <td>2.312147</td>\n",
       "      <td>2.602183</td>\n",
       "      <td>2.024745</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>0.400733</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.186347</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.220089</td>\n",
       "      <td>-2.929177</td>\n",
       "      <td>3.410922</td>\n",
       "      <td>0.694940</td>\n",
       "      <td>1.498068</td>\n",
       "      <td>2.302101</td>\n",
       "      <td>2.556029</td>\n",
       "      <td>1.916674</td>\n",
       "      <td>-0.097121</td>\n",
       "      <td>0.454645</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.198020</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.225614</td>\n",
       "      <td>-2.937813</td>\n",
       "      <td>3.387032</td>\n",
       "      <td>0.635237</td>\n",
       "      <td>1.499937</td>\n",
       "      <td>2.102797</td>\n",
       "      <td>2.516695</td>\n",
       "      <td>1.838733</td>\n",
       "      <td>-0.120700</td>\n",
       "      <td>0.500970</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.167465</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>0.061175</td>\n",
       "      <td>-2.850518</td>\n",
       "      <td>3.165786</td>\n",
       "      <td>2.037223</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>-1.059738</td>\n",
       "      <td>-1.063356</td>\n",
       "      <td>-0.784703</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>-3.202651</td>\n",
       "      <td>-0.069554</td>\n",
       "      <td>-3.181468</td>\n",
       "      <td>-0.296906</td>\n",
       "      <td>0.184863</td>\n",
       "      <td>0.946993</td>\n",
       "      <td>1.139885</td>\n",
       "      <td>0.571433</td>\n",
       "      <td>-0.817978</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.601879</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "2650     -2.086416     1.146897         -2.217884 -1.247235 -0.404519   \n",
       "2750     -2.086416     0.722336         -2.347124  0.720893  0.431804   \n",
       "5025     -1.443736     1.297482         -2.698896  3.154805  0.682660   \n",
       "5026     -0.564278     1.141629         -2.189783  0.265648 -0.153150   \n",
       "5050     -3.067350     1.585246         -2.476358  1.396136 -0.782847   \n",
       "6100     -1.646687     0.457765         -2.310616  1.833989  1.489480   \n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "6176     -2.086416     0.517310         -3.535766  4.003889  1.463660   \n",
       "6177     -1.815814     0.293287         -3.323528  3.380546  0.732305   \n",
       "6178     -1.815814     1.381461         -3.854778  5.508391  1.053051   \n",
       "6179     -1.748163     0.540625         -3.180434  2.709573  0.667897   \n",
       "6180     -1.849639     0.291101         -3.008869  3.443116  0.875766   \n",
       "6181     -2.086416     0.726924         -3.416935  3.153251  1.033799   \n",
       "6182     -1.849639     0.128079         -2.987955  3.182614  0.539878   \n",
       "6183     -1.883465     0.559954         -3.090460  3.785644  0.588896   \n",
       "6184     -2.390844     0.548491         -3.356360  3.778144  0.340973   \n",
       "6185     -1.748163     0.073370         -2.977437  2.994312  0.760935   \n",
       "6186     -2.357019     0.397078         -3.309947  3.641401  0.469220   \n",
       "6187     -1.815814     0.203483         -3.346815  2.572588  0.430980   \n",
       "6188     -1.917290     0.196464         -2.927473  3.375313  0.684771   \n",
       "6189     -1.748163     0.335249         -3.121586  3.246297  0.445623   \n",
       "6190     -2.052591     0.342238         -3.133392  3.608837  0.689004   \n",
       "6191     -2.018766     0.443371         -3.559795  2.056200  0.187604   \n",
       "6193     -1.781989     0.683279         -3.546341  3.042051  0.351990   \n",
       "6194     -1.917290     1.544363         -4.016045  5.989302 -0.963308   \n",
       "6195     -1.984940     0.181415         -3.033073  3.594376  0.729578   \n",
       "6196     -1.815814     0.273083         -2.951532  3.104686  0.981366   \n",
       "6197     -1.680513     0.353661         -3.003557  3.407620  0.675525   \n",
       "6198     -1.680513     0.220089         -2.929177  3.410922  0.694940   \n",
       "6199     -1.883465     0.225614         -2.937813  3.387032  0.635237   \n",
       "7675     -2.221718     0.061175         -2.850518  3.165786  2.037223   \n",
       "7700     -3.202651    -0.069554         -3.181468 -0.296906  0.184863   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "2650   1.025171   1.294520   0.671912   0.915515   -0.086042   -0.224453   \n",
       "2750   0.990499   1.166849   1.394751   2.292510   -0.188155    3.329386   \n",
       "5025   1.256502   1.666250   1.293021   0.393944   -0.067187    0.123279   \n",
       "5026   1.590055   1.613945   1.371150   0.589398   -0.085760    0.215901   \n",
       "5050   0.501491  -0.052399  -0.043444   0.101308    0.072443   -0.138037   \n",
       "6100   1.192236   0.211053   0.341799   1.351480   -0.204227    0.056343   \n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "6176   0.894764   1.277177   2.148085   1.600057    0.084708    2.187907   \n",
       "6177   1.483952   1.747604   2.166054   1.581048   -0.091321    1.378723   \n",
       "6178   1.129946   2.138966   2.557734   1.943433    0.196184    1.718829   \n",
       "6179   1.427806   2.207221   2.466783   1.705215   -0.208367    1.190854   \n",
       "6180   1.527562   2.186085   2.380435   1.732806   -0.111499    0.530213   \n",
       "6181   1.484045   2.193447   2.587556   2.013460   -0.123314    0.618552   \n",
       "6182   1.737777   2.324832   2.536665   1.386484    0.099575    0.452257   \n",
       "6183   1.794153   2.241939   2.621514   1.616357    0.058270    0.359513   \n",
       "6184   1.722154   2.379023   2.612524   1.583916    0.143382    0.585596   \n",
       "6185   1.561845   2.072250   2.185478   1.552148   -0.177150    0.826247   \n",
       "6186   1.452282   2.096269   2.496450   1.974161   -0.132036    0.559798   \n",
       "6187   1.597681   1.913452   2.183086   1.684987   -0.163895    1.556727   \n",
       "6188   1.560831   2.231691   2.590861   1.712657   -0.069786    0.474994   \n",
       "6189   1.492428   2.204697   2.533502   1.996110   -0.150345    0.469066   \n",
       "6190   1.649470   2.317912   2.603041   1.430013   -0.014496    0.546835   \n",
       "6191   1.580071   2.165352   2.341953   2.004631   -0.191814    2.098272   \n",
       "6193   1.475464   2.204002   2.544886   2.064549   -0.164838    0.821398   \n",
       "6194   1.394206   2.330022   2.670813   2.371793    0.122695    1.598140   \n",
       "6195   1.494870   1.970933   2.330075   1.676683   -0.125777    0.724950   \n",
       "6196   1.602459   2.125604   2.416907   1.457133   -0.151949    0.611140   \n",
       "6197   1.534201   2.312147   2.602183   2.024745   -0.092121    0.400733   \n",
       "6198   1.498068   2.302101   2.556029   1.916674   -0.097121    0.454645   \n",
       "6199   1.499937   2.102797   2.516695   1.838733   -0.120700    0.500970   \n",
       "7675   0.014291  -1.059738  -1.063356  -0.784703   -0.305750   -0.224453   \n",
       "7700   0.946993   1.139885   0.571433  -0.817978   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "2650  1.189286 -0.005000  2.772243  1.115004        -0.295389  -2.938450  \n",
       "2750  1.189286 -0.005000  2.772243  1.115004        -0.592600  -3.002504  \n",
       "5025  1.189286 -0.005000  2.772243  1.115004         1.165416  -3.066558  \n",
       "5026  1.189286  0.511177 -0.315157 -0.542158         0.865895  -2.810342  \n",
       "5050  1.189286 -0.005000  2.772243  1.115004         1.131952  -3.066558  \n",
       "6100  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "6176  1.189286  0.511177 -0.315157 -0.542158        -2.138584  -3.258721  \n",
       "6177  0.653021  0.838510 -0.526218 -0.723133        -2.150110  -3.194667  \n",
       "6178  1.991696  0.385280 -0.297588  0.932424        -2.174068  -3.098586  \n",
       "6179  0.118743  1.807917 -0.494322 -0.548433        -2.181193  -3.194667  \n",
       "6180 -0.685654  0.637074 -0.133199 -0.810993        -2.150112  -2.906423  \n",
       "6181  0.653021 -1.931225 -0.911963 -0.845437        -2.193176  -3.034531  \n",
       "6182 -1.490051 -1.024766 -0.172266  0.223191        -2.197299  -2.970477  \n",
       "6183 -0.953787 -2.082302 -0.201502 -0.616299        -2.121602  -3.066558  \n",
       "6184 -1.758184 -0.458230  2.701715  2.861413        -2.358212  -3.194667  \n",
       "6185 -0.149390  1.178432 -0.611095 -0.462470        -2.131853  -3.066558  \n",
       "6186  0.118743 -0.042770  1.294304 -0.484508        -2.046890  -3.739128  \n",
       "6187  0.653021  1.153252 -0.037504  1.043125        -2.208229  -3.130613  \n",
       "6188 -0.953787 -0.810742 -0.569309 -0.466338        -2.181012  -2.938450  \n",
       "6189  0.118743 -0.684844 -0.586289 -0.293244        -2.122811  -3.162640  \n",
       "6190 -1.490051  0.045358  0.376283  0.730359        -2.274529  -3.066558  \n",
       "6191  1.457418  1.354688 -0.154704 -0.408689        -2.146457  -3.386829  \n",
       "6193  0.653021 -0.672255  0.243650  0.372715        -2.064552  -3.643046  \n",
       "6194  1.457418  0.158666  1.560277  2.534052        -1.925129  -3.450884  \n",
       "6195  0.118743  0.611895 -0.655350 -0.499833        -2.156557  -2.970477  \n",
       "6196 -0.685654  1.191021 -0.735149 -0.820115        -2.190000  -2.842369  \n",
       "6197 -0.149390 -1.263971 -0.852681 -0.803915        -2.186347  -2.906423  \n",
       "6198 -0.417522 -1.037356 -0.821213 -0.755095        -2.198020  -2.938450  \n",
       "6199 -0.417522 -0.269384 -0.779043 -0.719338        -2.167465  -2.938450  \n",
       "7675  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "7700  1.189286 -0.005000  2.772243  1.115004        -1.601879  -3.162640  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "len(drop_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7719 rows x 16 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7719, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了33行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.2365368942338734\n",
      "RMSE: 0.48635058777991974\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE:  0.24843292023773117\n",
    "# RMSE:  0.49843045677178593"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.498降低到了0.486，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7719 rows x 16 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7719, 14)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.738888469431397, 0.7431917103560932)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.24095547445500337\n",
      "RMSE: 0.4908721569360024\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>-0.227469</td>\n",
       "      <td>1.047950</td>\n",
       "      <td>0.419490</td>\n",
       "      <td>-0.490863</td>\n",
       "      <td>-0.845073</td>\n",
       "      <td>-0.677817</td>\n",
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       "      <th>4</th>\n",
       "      <td>-1.181040</td>\n",
       "      <td>0.132213</td>\n",
       "      <td>1.700762</td>\n",
       "      <td>0.864334</td>\n",
       "      <td>0.305876</td>\n",
       "      <td>0.042785</td>\n",
       "      <td>-0.695446</td>\n",
       "      <td>-0.530648</td>\n",
       "      <td>0.390678</td>\n",
       "      <td>-1.373559</td>\n",
       "      <td>-0.684244</td>\n",
       "      <td>-0.126623</td>\n",
       "      <td>-0.934499</td>\n",
       "      <td>0.187668</td>\n",
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       "      <th>7714</th>\n",
       "      <td>-1.828045</td>\n",
       "      <td>0.204511</td>\n",
       "      <td>0.087075</td>\n",
       "      <td>-0.137277</td>\n",
       "      <td>-3.611235</td>\n",
       "      <td>-0.232090</td>\n",
       "      <td>0.198096</td>\n",
       "      <td>1.399704</td>\n",
       "      <td>0.710092</td>\n",
       "      <td>-1.356931</td>\n",
       "      <td>-0.861351</td>\n",
       "      <td>-0.014907</td>\n",
       "      <td>-0.133469</td>\n",
       "      <td>-0.036297</td>\n",
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       "      <th>7715</th>\n",
       "      <td>-1.917145</td>\n",
       "      <td>-0.185958</td>\n",
       "      <td>-0.908207</td>\n",
       "      <td>-0.975988</td>\n",
       "      <td>-3.522399</td>\n",
       "      <td>0.521996</td>\n",
       "      <td>-0.256674</td>\n",
       "      <td>1.343472</td>\n",
       "      <td>-0.246521</td>\n",
       "      <td>0.404696</td>\n",
       "      <td>-0.731927</td>\n",
       "      <td>0.547065</td>\n",
       "      <td>-0.274555</td>\n",
       "      <td>-0.114590</td>\n",
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       "    <tr>\n",
       "      <th>7716</th>\n",
       "      <td>-1.945816</td>\n",
       "      <td>-0.417947</td>\n",
       "      <td>-1.306099</td>\n",
       "      <td>-1.002492</td>\n",
       "      <td>-3.298725</td>\n",
       "      <td>-0.026787</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>1.672442</td>\n",
       "      <td>-0.081839</td>\n",
       "      <td>0.324312</td>\n",
       "      <td>-0.520419</td>\n",
       "      <td>0.345995</td>\n",
       "      <td>-0.210014</td>\n",
       "      <td>-0.158449</td>\n",
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       "      <th>7717</th>\n",
       "      <td>-1.864801</td>\n",
       "      <td>-0.594864</td>\n",
       "      <td>-1.441804</td>\n",
       "      <td>-0.743734</td>\n",
       "      <td>-3.012536</td>\n",
       "      <td>-1.071030</td>\n",
       "      <td>-0.124629</td>\n",
       "      <td>2.227065</td>\n",
       "      <td>0.116584</td>\n",
       "      <td>0.182574</td>\n",
       "      <td>-0.114871</td>\n",
       "      <td>-0.153969</td>\n",
       "      <td>-0.112684</td>\n",
       "      <td>-0.181380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>8.784503</td>\n",
       "      <td>3.163045</td>\n",
       "      <td>4.467119</td>\n",
       "      <td>4.805559</td>\n",
       "      <td>9.278716</td>\n",
       "      <td>8.369438</td>\n",
       "      <td>5.980771</td>\n",
       "      <td>9.079706</td>\n",
       "      <td>6.199409</td>\n",
       "      <td>3.802075</td>\n",
       "      <td>0.179508</td>\n",
       "      <td>1.663953</td>\n",
       "      <td>-0.335985</td>\n",
       "      <td>0.833460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.188875  3.105274 -0.174420  0.232023  0.614357 -0.712495 -1.301660   \n",
       "1    -1.041580 -0.102778  1.236504  0.435599  0.429038 -0.683036 -1.097555   \n",
       "2    -1.122112 -0.891416  0.471348  0.462210  0.594114 -1.437138 -1.028615   \n",
       "3    -0.787025  1.227941  1.628529  0.750534  0.480516 -0.491080 -0.976939   \n",
       "4    -1.181040  0.132213  1.700762  0.864334  0.305876  0.042785 -0.695446   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7714 -1.828045  0.204511  0.087075 -0.137277 -3.611235 -0.232090  0.198096   \n",
       "7715 -1.917145 -0.185958 -0.908207 -0.975988 -3.522399  0.521996 -0.256674   \n",
       "7716 -1.945816 -0.417947 -1.306099 -1.002492 -3.298725 -0.026787 -0.182500   \n",
       "7717 -1.864801 -0.594864 -1.441804 -0.743734 -3.012536 -1.071030 -0.124629   \n",
       "7718  8.784503  3.163045  4.467119  4.805559  9.278716  8.369438  5.980771   \n",
       "\n",
       "             7         8         9        10        11        12        13  \n",
       "0    -0.395921  0.169397  0.661308 -0.334142  0.350072 -0.427288  1.102016  \n",
       "1    -0.397473 -0.026081  0.502019  0.201790 -0.256293 -0.824149  0.247166  \n",
       "2     0.193530  0.127245  0.225650  0.100329 -0.208413 -0.926234  0.177910  \n",
       "3    -0.864368 -0.227469  1.047950  0.419490 -0.490863 -0.845073 -0.677817  \n",
       "4    -0.530648  0.390678 -1.373559 -0.684244 -0.126623 -0.934499  0.187668  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "7714  1.399704  0.710092 -1.356931 -0.861351 -0.014907 -0.133469 -0.036297  \n",
       "7715  1.343472 -0.246521  0.404696 -0.731927  0.547065 -0.274555 -0.114590  \n",
       "7716  1.672442 -0.081839  0.324312 -0.520419  0.345995 -0.210014 -0.158449  \n",
       "7717  2.227065  0.116584  0.182574 -0.114871 -0.153969 -0.112684 -0.181380  \n",
       "7718  9.079706  6.199409  3.802075  0.179508  1.663953 -0.335985  0.833460  \n",
       "\n",
       "[7719 rows x 14 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7719, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.222742</td>\n",
       "      <td>0.263311</td>\n",
       "      <td>-0.354324</td>\n",
       "      <td>0.201683</td>\n",
       "      <td>-0.104739</td>\n",
       "      <td>0.393294</td>\n",
       "      <td>0.422638</td>\n",
       "      <td>0.399707</td>\n",
       "      <td>0.344365</td>\n",
       "      <td>0.199209</td>\n",
       "      <td>0.161564</td>\n",
       "      <td>0.024391</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>0.075389</td>\n",
       "      <td>0.068689</td>\n",
       "      <td>0.123937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.176182</td>\n",
       "      <td>0.264489</td>\n",
       "      <td>-0.113884</td>\n",
       "      <td>0.138779</td>\n",
       "      <td>0.255222</td>\n",
       "      <td>-0.076576</td>\n",
       "      <td>-0.145124</td>\n",
       "      <td>-0.186183</td>\n",
       "      <td>-0.187085</td>\n",
       "      <td>0.005605</td>\n",
       "      <td>-0.060720</td>\n",
       "      <td>0.174841</td>\n",
       "      <td>0.062611</td>\n",
       "      <td>0.572606</td>\n",
       "      <td>0.582067</td>\n",
       "      <td>0.012661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.035115</td>\n",
       "      <td>0.371731</td>\n",
       "      <td>0.122479</td>\n",
       "      <td>-0.090039</td>\n",
       "      <td>0.359309</td>\n",
       "      <td>0.196599</td>\n",
       "      <td>0.050196</td>\n",
       "      <td>-0.139835</td>\n",
       "      <td>-0.191900</td>\n",
       "      <td>0.303063</td>\n",
       "      <td>-0.086435</td>\n",
       "      <td>0.464752</td>\n",
       "      <td>0.363850</td>\n",
       "      <td>-0.293949</td>\n",
       "      <td>-0.242831</td>\n",
       "      <td>0.133229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.145043</td>\n",
       "      <td>-0.114328</td>\n",
       "      <td>0.010787</td>\n",
       "      <td>-0.022730</td>\n",
       "      <td>0.069970</td>\n",
       "      <td>-0.267264</td>\n",
       "      <td>-0.078085</td>\n",
       "      <td>0.237171</td>\n",
       "      <td>0.337363</td>\n",
       "      <td>-0.364178</td>\n",
       "      <td>0.459535</td>\n",
       "      <td>0.384470</td>\n",
       "      <td>0.459891</td>\n",
       "      <td>0.026564</td>\n",
       "      <td>0.064236</td>\n",
       "      <td>-0.069460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.553570</td>\n",
       "      <td>-0.119346</td>\n",
       "      <td>0.414334</td>\n",
       "      <td>0.270975</td>\n",
       "      <td>-0.021542</td>\n",
       "      <td>0.071159</td>\n",
       "      <td>0.067682</td>\n",
       "      <td>0.067230</td>\n",
       "      <td>0.081974</td>\n",
       "      <td>0.203411</td>\n",
       "      <td>0.014358</td>\n",
       "      <td>-0.091487</td>\n",
       "      <td>0.033391</td>\n",
       "      <td>0.157431</td>\n",
       "      <td>0.178319</td>\n",
       "      <td>0.552467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.163904</td>\n",
       "      <td>0.208568</td>\n",
       "      <td>-0.055769</td>\n",
       "      <td>0.166646</td>\n",
       "      <td>0.630398</td>\n",
       "      <td>-0.084256</td>\n",
       "      <td>-0.115130</td>\n",
       "      <td>-0.009862</td>\n",
       "      <td>0.103554</td>\n",
       "      <td>0.013371</td>\n",
       "      <td>0.428871</td>\n",
       "      <td>-0.210514</td>\n",
       "      <td>-0.458194</td>\n",
       "      <td>-0.123691</td>\n",
       "      <td>-0.101349</td>\n",
       "      <td>-0.078992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.365585</td>\n",
       "      <td>-0.052028</td>\n",
       "      <td>0.078648</td>\n",
       "      <td>-0.087867</td>\n",
       "      <td>0.051706</td>\n",
       "      <td>0.065798</td>\n",
       "      <td>0.109443</td>\n",
       "      <td>0.116339</td>\n",
       "      <td>0.064194</td>\n",
       "      <td>0.446661</td>\n",
       "      <td>-0.055199</td>\n",
       "      <td>-0.137450</td>\n",
       "      <td>0.193075</td>\n",
       "      <td>0.096424</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>-0.717746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.265292</td>\n",
       "      <td>-0.279226</td>\n",
       "      <td>0.006281</td>\n",
       "      <td>0.658992</td>\n",
       "      <td>-0.181603</td>\n",
       "      <td>-0.010895</td>\n",
       "      <td>-0.189539</td>\n",
       "      <td>-0.228418</td>\n",
       "      <td>-0.142294</td>\n",
       "      <td>0.329392</td>\n",
       "      <td>0.325918</td>\n",
       "      <td>0.064108</td>\n",
       "      <td>0.143243</td>\n",
       "      <td>-0.070112</td>\n",
       "      <td>-0.129384</td>\n",
       "      <td>-0.122030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.260557</td>\n",
       "      <td>0.114002</td>\n",
       "      <td>0.184736</td>\n",
       "      <td>-0.514467</td>\n",
       "      <td>-0.118458</td>\n",
       "      <td>0.006525</td>\n",
       "      <td>-0.093250</td>\n",
       "      <td>-0.117852</td>\n",
       "      <td>-0.031030</td>\n",
       "      <td>0.280630</td>\n",
       "      <td>0.520935</td>\n",
       "      <td>-0.379582</td>\n",
       "      <td>0.203918</td>\n",
       "      <td>0.091311</td>\n",
       "      <td>0.059847</td>\n",
       "      <td>0.187024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.039571</td>\n",
       "      <td>-0.005249</td>\n",
       "      <td>0.117088</td>\n",
       "      <td>-0.231484</td>\n",
       "      <td>-0.297357</td>\n",
       "      <td>-0.123173</td>\n",
       "      <td>-0.064410</td>\n",
       "      <td>0.011790</td>\n",
       "      <td>0.075488</td>\n",
       "      <td>0.310984</td>\n",
       "      <td>0.169668</td>\n",
       "      <td>0.599610</td>\n",
       "      <td>-0.569295</td>\n",
       "      <td>0.044760</td>\n",
       "      <td>0.071209</td>\n",
       "      <td>-0.036481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.229190</td>\n",
       "      <td>0.369807</td>\n",
       "      <td>0.186680</td>\n",
       "      <td>0.122171</td>\n",
       "      <td>-0.328840</td>\n",
       "      <td>0.235760</td>\n",
       "      <td>0.282023</td>\n",
       "      <td>-0.140312</td>\n",
       "      <td>-0.417206</td>\n",
       "      <td>-0.404072</td>\n",
       "      <td>0.329369</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>-0.061243</td>\n",
       "      <td>-0.032764</td>\n",
       "      <td>0.053543</td>\n",
       "      <td>-0.210616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.036390</td>\n",
       "      <td>-0.638793</td>\n",
       "      <td>-0.067746</td>\n",
       "      <td>-0.205118</td>\n",
       "      <td>0.325155</td>\n",
       "      <td>0.465504</td>\n",
       "      <td>0.256466</td>\n",
       "      <td>-0.075083</td>\n",
       "      <td>-0.236228</td>\n",
       "      <td>-0.094371</td>\n",
       "      <td>0.183414</td>\n",
       "      <td>0.155599</td>\n",
       "      <td>-0.077864</td>\n",
       "      <td>0.138497</td>\n",
       "      <td>0.042510</td>\n",
       "      <td>0.022637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352690</td>\n",
       "      <td>0.055647</td>\n",
       "      <td>0.669917</td>\n",
       "      <td>0.084548</td>\n",
       "      <td>0.103422</td>\n",
       "      <td>0.320851</td>\n",
       "      <td>-0.078004</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>0.417550</td>\n",
       "      <td>-0.182148</td>\n",
       "      <td>-0.123814</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>-0.074508</td>\n",
       "      <td>0.139466</td>\n",
       "      <td>-0.074121</td>\n",
       "      <td>-0.196884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.043159</td>\n",
       "      <td>0.060647</td>\n",
       "      <td>0.016026</td>\n",
       "      <td>0.005236</td>\n",
       "      <td>0.019374</td>\n",
       "      <td>-0.188307</td>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.126086</td>\n",
       "      <td>-0.141133</td>\n",
       "      <td>0.039357</td>\n",
       "      <td>0.012209</td>\n",
       "      <td>0.014843</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.666280</td>\n",
       "      <td>-0.675011</td>\n",
       "      <td>-0.016761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0  -0.222742  0.263311 -0.354324  0.201683 -0.104739  0.393294  0.422638   \n",
       "1  -0.176182  0.264489 -0.113884  0.138779  0.255222 -0.076576 -0.145124   \n",
       "2   0.035115  0.371731  0.122479 -0.090039  0.359309  0.196599  0.050196   \n",
       "3   0.145043 -0.114328  0.010787 -0.022730  0.069970 -0.267264 -0.078085   \n",
       "4   0.553570 -0.119346  0.414334  0.270975 -0.021542  0.071159  0.067682   \n",
       "5   0.163904  0.208568 -0.055769  0.166646  0.630398 -0.084256 -0.115130   \n",
       "6   0.365585 -0.052028  0.078648 -0.087867  0.051706  0.065798  0.109443   \n",
       "7  -0.265292 -0.279226  0.006281  0.658992 -0.181603 -0.010895 -0.189539   \n",
       "8  -0.260557  0.114002  0.184736 -0.514467 -0.118458  0.006525 -0.093250   \n",
       "9   0.039571 -0.005249  0.117088 -0.231484 -0.297357 -0.123173 -0.064410   \n",
       "10  0.229190  0.369807  0.186680  0.122171 -0.328840  0.235760  0.282023   \n",
       "11 -0.036390 -0.638793 -0.067746 -0.205118  0.325155  0.465504  0.256466   \n",
       "12 -0.352690  0.055647  0.669917  0.084548  0.103422  0.320851 -0.078004   \n",
       "13  0.043159  0.060647  0.016026  0.005236  0.019374 -0.188307  0.143595   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.399707  0.344365  0.199209  0.161564  0.024391 -0.007817  0.075389   \n",
       "1  -0.186183 -0.187085  0.005605 -0.060720  0.174841  0.062611  0.572606   \n",
       "2  -0.139835 -0.191900  0.303063 -0.086435  0.464752  0.363850 -0.293949   \n",
       "3   0.237171  0.337363 -0.364178  0.459535  0.384470  0.459891  0.026564   \n",
       "4   0.067230  0.081974  0.203411  0.014358 -0.091487  0.033391  0.157431   \n",
       "5  -0.009862  0.103554  0.013371  0.428871 -0.210514 -0.458194 -0.123691   \n",
       "6   0.116339  0.064194  0.446661 -0.055199 -0.137450  0.193075  0.096424   \n",
       "7  -0.228418 -0.142294  0.329392  0.325918  0.064108  0.143243 -0.070112   \n",
       "8  -0.117852 -0.031030  0.280630  0.520935 -0.379582  0.203918  0.091311   \n",
       "9   0.011790  0.075488  0.310984  0.169668  0.599610 -0.569295  0.044760   \n",
       "10 -0.140312 -0.417206 -0.404072  0.329369  0.011250 -0.061243 -0.032764   \n",
       "11 -0.075083 -0.236228 -0.094371  0.183414  0.155599 -0.077864  0.138497   \n",
       "12 -0.062740  0.417550 -0.182148 -0.123814  0.028249 -0.074508  0.139466   \n",
       "13  0.126086 -0.141133  0.039357  0.012209  0.014843  0.011680  0.666280   \n",
       "\n",
       "          14        15  \n",
       "0   0.068689  0.123937  \n",
       "1   0.582067  0.012661  \n",
       "2  -0.242831  0.133229  \n",
       "3   0.064236 -0.069460  \n",
       "4   0.178319  0.552467  \n",
       "5  -0.101349 -0.078992  \n",
       "6   0.172967 -0.717746  \n",
       "7  -0.129384 -0.122030  \n",
       "8   0.059847  0.187024  \n",
       "9   0.071209 -0.036481  \n",
       "10  0.053543 -0.210616  \n",
       "11  0.042510  0.022637  \n",
       "12 -0.074121 -0.196884  \n",
       "13 -0.675011 -0.016761  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2524355 , 0.12934125, 0.0891994 , 0.08255682, 0.07232587,\n",
       "       0.06844881, 0.05650669, 0.05073909, 0.05063498, 0.04080831,\n",
       "       0.03431659, 0.0232422 , 0.01804316, 0.01314993])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
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   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7313422308938741, 0.7313422308938741)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'n_estimators':list(range(100,200,10)), \"max_depth\":list(range(5,15,2))}\n",
    "model = RandomForestRegressor()\n",
    "grid_search = GridSearchCV(model, param_grid, cv=3, scoring='r2')\n",
    "GS = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 13, 'n_estimators': 110}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=GS.best_params_['n_estimators'],\n",
    "                         max_depth=GS.best_params_['max_depth']).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9676263570054149, 0.8735001910246204)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.15967418671718914"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n",
    "cross_val_score(rfr, X_test, Y_test, cv=4,scoring = \"neg_mean_squared_error\").mean()     #计算结果是给MSE加负号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "测试集上的MSE由原来的0.25降到了0.16，R²由原来的0.75上升到了0.87。\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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